Modeling network signaling in a mobile network based on elapsed time

ABSTRACT

The disclosed technology includes systems and methods for optimizing network traffic management in a mobile network. One method includes determining if a mobile application executing on a mobile device is associated with network signaling requiring a corresponding radio connection. At least a portion of the network signaling caused by the transactions is filtered. The filtered network signaling does not cause a corresponding radio connection. A signaling efficiency is calculated that indicates a total number of the radio connections that are saved as a result of the filtering.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/832,955 filed Aug. 21, 2015 entitled “MODELING NETWORK SIGNALING IN AMOBILE NETWORK BASED ON ELAPSED TIME”, which claims the benefit of U.S.Provisional Patent Application No. 62/040,260 filed Aug. 21, 2014entitled “MODELING NETWORK SIGNALING IN A MOBILE NETWORK BASED ONELAPSED TIME”. The content of each of the aforementioned applications isincorporated by reference herein in its entirety.

BACKGROUND

The present invention relates to modeling network signaling in a mobilenetwork, and more specifically, to optimizing network traffic managementin a mobile network.

DESCRIPTION OF RELATED ART

In order to address mobile network congestion, it is ideal to be able toenforce network management policies or corrective actions on the deviceswhich are in specific congested areas. Unfortunately, the correctiveactions are currently indiscriminately applied to the devices. Thispresents a challenge as indiscriminate application of corrective actionscan negatively impact end-user experience.

Accordingly, a need exists for modeling network signaling in a mobilenetwork in order to more precisely apply corrective actions in congestedareas.

BRIEF SUMMARY

According to one embodiment of the present invention, one methodincludes determining if a mobile application executing on a mobiledevice is associated with network signaling requiring a correspondingradio connection. At least a portion of the network signaling caused bythe transactions is filtered. The filtered network signaling does notcause a corresponding radio connection. A signaling efficiency iscalculated that indicates a total number of the radio connections thatare saved as a result of the filtering.

A system includes a mobile network optimization module configured to:determine if a mobile application executing on a mobile device isassociated with network signaling requiring a corresponding radioconnection, filter at least a portion of the network signaling caused bythe transactions, wherein the filtered network signaling does not causea corresponding radio connection; A signaling efficiency moduleconfigured to calculate a signaling efficiency indicating a total numberof the radio connections that are saved as a result of the filtering.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example diagram of a system where a host serverfacilitates management of traffic, content caching, and/or resourceconservation between mobile devices (e.g., wireless devices), anapplication server or content provider, or other servers such as an adserver, promotional content server, or an e-coupon server in a wirelessnetwork (or broadband network) for resource conservation.

FIG. 1A-1 illustrates an example diagram illustrating a generalarchitectural overview of a distributed Network optimization system.

FIG. 1B illustrates an example diagram of a proxy and cache systemdistributed between the host server and device which facilitates networktraffic management between a device, an application server or contentprovider, or other servers such as an ad server, promotional contentserver, or an e-coupon server for resource conservation and contentcaching.

FIG. 1C illustrates an example diagram of the logical architecture of adistributed proxy and cache system.

FIG. 1D illustrates an example diagram showing the architecture ofclient side components in a distributed proxy and cache system.

FIG. 1E illustrates a diagram of the example components on the serverside of the distributed proxy and cache system.

FIG. 1F illustrates an example diagram showing data flows betweenexample client side components in a distributed proxy and cache system.

FIG. 2A depicts a block diagram illustrating an example of client-sidecomponents in a distributed proxy and cache system residing on a mobiledevice (e.g., wireless device) that manages traffic in a wirelessnetwork (or broadband network) for resource conservation, contentcaching, and/or traffic management. The client-side proxy (or localproxy) can further categorize mobile traffic and/or implement deliverypolicies based on application behavior, content priority, user activity,and/or user expectations, for example, for further use in facilitatingaligned data transfer to optimize connections established at the mobiledevice.

FIG. 2B depicts a block diagram illustrating a further example ofcomponents in the cache system shown in the example of FIG. 2A which iscapable of caching and adapting caching strategies for mobileapplication behavior and/or network conditions. Components capable ofdetecting long poll requests and managing caching of long polls are alsoillustrated.

FIG. 2C depicts a block diagram illustrating additional components inthe application behavior detector and the caching policy manager in thecache system shown in the example of FIG. 2A which is further capable ofdetecting cache defeat and perform caching of content addressed byidentifiers intended to defeat cache.

FIG. 2D depicts a block diagram illustrating examples of additionalcomponents in the local cache shown in the example of FIG. 2A which isfurther capable of performing mobile traffic categorization and policyimplementation based on application behavior and/or user activity.

FIG. 2E depicts a block diagram illustrating examples of additionalcomponents in the traffic shaping engine and the application behaviordetector shown in the example of FIG. 2A which are further capable offacilitating alignment of incoming data transfer to a mobile orbroadband device, or its user, to optimize the number of connectionsthat need to be established for receiving data over the wireless networkor broadband network.

FIG. 3A depicts a block diagram illustrating an example of server-sidecomponents in a distributed proxy and cache system that manages trafficin a wireless network (or broadband network) for resource conservation,content caching, and/or traffic management. The server-side proxy (orproxy server) can further categorize mobile traffic and/or implementdelivery policies based on application behavior, content priority, useractivity, and/or user expectations, for example, for further use inaligning data transfer to optimize connections established for wirelesstransmission to a mobile device.

FIG. 3B depicts a block diagram illustrating a further example ofcomponents in the caching policy manager in the cache system shown inthe example of FIG. 3A which is capable of caching and adapting cachingstrategies for mobile application behavior and/or network conditions.Components capable of detecting long poll requests and managing cachingof long polls are also illustrated.

FIG. 3C depicts a block diagram illustrating another example ofcomponents in the proxy system shown in the example of FIG. 3A which isfurther capable of managing and detecting cache defeating mechanisms andmonitoring content sources.

FIG. 3D depicts a block diagram illustrating examples of additionalcomponents in proxy server shown in the example of FIG. 3A which isfurther capable of performing mobile traffic categorization and policyimplementation based on application behavior and/or traffic priority.

FIG. 3E depicts a block diagram illustrating examples of additionalcomponents in the traffic shaping engine of the example of FIG. 3A whichis further capable of aligning data transfer to a mobile or broadbanddevice, or other recipient, to optimize connections established fortransmission in a wireless network or broadband network.

FIG. 4 depicts a flow diagram illustrating an example process fordistributed content caching between a mobile device (e.g., any wirelessdevice) and remote proxy and the distributed management of contentcaching.

FIG. 5 depicts a timing diagram showing how data requests from a mobiledevice (e.g., any wireless device) to an application server/contentprovider in a wireless network (or broadband network) can be coordinatedby a distributed proxy system in a manner such that network and batteryresources are conserved through using content caching and monitoringperformed by the distributed proxy system.

FIG. 6 depicts a table showing examples of different traffic orapplication category types which can be used in implementing networkaccess and content delivery policies.

FIG. 7 depicts a table showing examples of different content categorytypes which can be used in implementing network access and contentdelivery policies.

FIG. 8 depicts an interaction diagram showing how polls having datarequests from a mobile device (e.g., any wireless device) to anapplication server/content provider over a wireless network (orbroadband network) can be can be cached on the local proxy and managedby the distributed caching system.

FIG. 9 depicts a flow diagram illustrating an example process formodeling signaling of a mobile device (e.g., any wireless device) in amobile network.

FIG. 10 depicts another flow diagram illustrating an example process formodeling signaling of a mobile device (e.g., any wireless device) in amobile network.

FIG. 11A-FIG. 16D depict example log/reporting data field calculationsfor use in determining general connection and time calculations.

FIGS. 17A and 17B illustrate an example of calculating connection flagsand connection time intervals and an example radio up interval,respectively.

FIG. 18 depicts an example scheme illustrating logs over a period oftime.

FIGS. 19A and 19B graphically illustrates a long poll procedure forsplitting one netlog item into two netlog items and the conditions whichmust be true in order for the netlog to be split in two parts,respectively.

FIGS. 20 and 21 graphically illustrates examples calculations of theTIME_ON_NOT_CHARGING field and the TIME_ON_NOT_CHARGING fields,respectively.

FIG. 22 depicts example measurement points from which a log/reportingdata analysis core module can perform measurements for modeling signalsin a data network.

FIGS. 23A-23E respectively depict graphics illustrations of the outputmetrics which can be used in various embodiments of the log/reportingdata analysis core module.

FIGS. 24A-24J graphically illustrate various calculations of exampleoutput metrics that can be used in embodiments of the log/reporting dataanalysis core module.

FIG. 25 depicts an example diagram illustrating a general architecturaloverview of a distributed Network optimization system including themeasurement points from which a log/reporting data analysis core modulecan perform measurements for modeling signals in the data network.

FIGS. 26A-26N show additional examples of and/or alternative outputmetrics that the log/reporting data analysis core module can adapt.

FIG. 27 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

FIG. 28 shows the relationship between the effective current draw andtime connected as a portion of total time for sample WCDMA test runs.

FIG. 29 shows the same data as FIG. 28 but evaluated in terms of pairsof results including one with optimization and one without optimization.

FIG. 30 shows a cross-plot of the slopes (β) versus the TC optimizations(overall alpha) for SbS runs reflecting varying amounts of optimization.

FIG. 31 shows a cross-plot of overhead versus a according to anembodiment of the subject matter described herein.

FIG. 32 shows a plot of slopes resulting from no-optimization versusoptimization in the same graphical representation as used in FIG. 30.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the description. References to “one embodiment” or“an embodiment” in the present disclosure can be, but not necessarilyare, references to the same embodiment and such references mean at leastone of the embodiments.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

The disclosed technology includes systems and methods for modelingsignaling and/or connections in a mobile network, and specifically, thebenefits of any optimization technique on the traffic including signalsand/or connections in the mobile network. Embodiments can allocatesignaling to specific applications (e.g., to determine whichapplications are chatty and which can cause problematic signaling),and/or to further model the optimizations or savings utilizing thedisclosed traffic optimization technology.

In some embodiments, the disclosed technology recognizes that signalingin the mobile network can occur when, for example, radio connections getset up (e.g., connected) or torn down (e.g., disconnected). Each radioconnection can be used for one or more transactions/data transfers,which can source from one or more applications. The disclosed technologydefines whether a transaction causes a connection (and thus signaling),and can further model, compute, or otherwise quantify the signaling orconnection savings resulting from any traffic optimization techniquesutilized in the signaling or data path.

In some embodiments, to enable or enhance the performance of the datatraffic and signal optimization for the network, the disclosedtechnology includes one or more fields (e.g., an expanded “log/reportingdata” fields) that are calculated by, for example, a log/reporting dataanalysis core module, to define and identify at least: (1) whether atransaction causes a connection (and thus signaling); and (2) the numberof connections that are reduced or saved by the disclosed embodiments ofdistributed caching and proxy system.

FIG. 1A illustrates an example diagram of a system where a host server100 facilitates management of traffic, content caching, and/or resourceconservation between mobile devices (e.g., wireless devices 150 orclient devices 150), and an application server or content provider 110,or other servers such as an ad server 120A, promotional content server120B, or an e-coupon server 120C in a wireless network (or broadbandnetwork) for resource conservation. The host server 100 can furtherbecome aware of mobile device radio states for use in selecting asuitable communications channel for sending messages generated by thehost server or other control signals and facilitate using a user as anend point for profiling and optimizing the delivery of content and datain a wireless network.

The mobile/client devices 150 can be any system and/or device, and/orany combination of devices/systems that is able to establish aconnection, including wired, wireless, cellular connections with anotherdevice, a server and/or other systems such as host server 100 and/orapplication server/content provider 110. Client/mobile devices 150 willtypically include a display and/or other output functionalities topresent information and data exchanged between among the devices 150and/or the host server 100 and/or application server/content provider110. The application server/content provider 110 can by any serverincluding third party servers or service/content providers furtherincluding advertisement, promotional content, publication, or electroniccoupon servers or services. Similarly, separate advertisement servers120A, promotional content servers 120B, and/or e-Coupon servers 120C asapplication servers or content providers are illustrated by way ofexample.

For example, the client/mobile devices 150 can include mobile, hand heldor portable devices, wireless devices, or non-portable devices and canbe any of, but not limited to, a server desktop, a desktop computer, acomputer cluster, or portable devices, including a notebook, a laptopcomputer, a handheld computer, a palmtop computer, a mobile phone, acell phone, a smart phone, a PDA, a Blackberry device, a Palm device,any tablet, a phablet (a class of smart phones with larger screen sizesbetween a typical smart phone and tablet), a handheld tablet (e.g., aniPad, the Galaxy series, the Nexus, the Kindles, Kindle Fires, anyAndroid-based tablet, Windows-based tablet, Amazon-based, or any othertablet), any portable readers/reading devices, a hand held console, ahand held gaming device or console, a head mounted device, a headmounted display, a thin client or any Super Phone such as the iPhone,and/or any other portable, mobile, hand held devices, or fixed wirelessinterface such as a M2M device, etc. In one embodiment, the clientdevices 150 (or mobile devices 150), host server 100, and applicationserver 110 are coupled via a network 106 and/or a network 108. In someembodiments, the devices 150 and host server 100 may be directlyconnected to one another.

The input mechanism on client devices 150 can include touch screenkeypad (including single touch, multi-touch, gesture sensing in 2D or3D, etc.), a physical keypad, a mouse, a pointer, a track pad, a stylus,a stylus detector/sensor/receptor, motion detector/sensor (e.g.,including 1-axis, 2-axis, 3-axis accelerometer, etc.), a facedetector/recognizer, a retinal detector/scanner, a light sensor,capacitance sensor, resistance sensor, temperature sensor, proximitysensor, a piezoelectric device, device orientation detector (e.g.,electronic compass, tilt sensor, rotation sensor, gyroscope,accelerometer), or any combination of the above.

Signals received or detected indicating user activity at client devices150 through one or more of the above input mechanism, or others, can beused in the disclosed technology in acquiring context awareness at theclient device 150. Context awareness at client devices 150 generallyincludes, by way of example but not limitation, client device 150operation or state acknowledgement, management, useractivity/behavior/interaction awareness, detection, sensing, tracking,trending, and/or application (e.g., mobile applications) type, behavior,activity, operating state, etc.

Context awareness in the present disclosure also includes knowledge anddetection of network side contextual data and can include networkinformation such as network capacity, bandwidth, traffic, type ofnetwork/connectivity, and/or any other operational state data. Networkside contextual data can be received from and/or queried from networkservice providers (e.g., cell provider 112 and/or Internet serviceproviders) of the network 106 and/or network 108 (e.g., by the hostserver and/or devices 150). In addition to application context awarenessas determined from the client 150 side, the application contextawareness may also be received from or obtained/queried from therespective application/service providers 110 (by the host 100 and/orclient devices 150).

The host server 100 can use, for example, contextual informationobtained for client devices 150, networks 106/108, applications (e.g.,mobile applications), application server/provider 110, or anycombination of the above, to manage the traffic in the system to satisfydata needs of the client devices 150 (e.g., to satisfy application orany other request including HTTP request). In one embodiment, thetraffic is managed by the host server 100 to satisfy data requests madein response to explicit or non-explicit user 103 requests and/ordevice/application maintenance tasks. The traffic can be managed suchthat network consumption, for example, use of the cellular network isconserved for effective and efficient bandwidth utilization. Inaddition, the host server 100 can manage and coordinate such traffic inthe system such that use of device 150 side resources (e.g., includingbut not limited to battery power consumption, radio use,processor/memory use) are optimized with a general philosophy forresource conservation while still optimizing performance and userexperience.

For example, in context of battery conservation, the device 150 canobserve user activity (for example, by observing user keystrokes,backlight status, or other signals via one or more input mechanisms,etc.) and alters device 150 behaviors. The device 150 can also requestthe host server 100 to alter the behavior for network resourceconsumption based on user activity or behavior.

In one embodiment, the traffic management for resource conservation isperformed using a distributed system between the host server 100 andclient device 150. The distributed system can include proxy server andcache components on the server side 100 and on the device/client side,for example, as shown by the server cache 135 on the server 100 side andthe local cache 185 on the client 150 side.

Functions and techniques disclosed for context aware traffic managementfor resource conservation in networks (e.g., network 106 and/or 108) anddevices 150, reside in a distributed proxy and/or cache system (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation) (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation). Theproxy and cache system can be distributed between, and reside on, agiven client device 150 in part or in whole and/or host server 100 inpart or in whole. The distributed proxy and/or cache system (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation) (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation) areillustrated with further reference to the example diagram shown in FIG.1C. Functions and techniques performed by the (distributed) proxy and/orcache components in the client device 150, the host server 100, and therelated components therein are described, respectively, in detail withfurther reference to the examples of FIG. 2-5.

In one embodiment, client devices 150 communicate with the host server100 and/or the application server 110 over network 106, which can be acellular network and/or a broadband network. To facilitate overalltraffic management between devices 150 and various applicationservers/content providers 110 to implement network (bandwidthutilization) and device resource (e.g., battery consumption), the hostserver 100 can communicate with the application server/providers 110over the network 108, which can include the Internet (e.g., a broadbandnetwork).

In general, the networks 106 and/or 108, over which the client devices150, the host server 100, and/or application server 110 communicate, maybe a cellular network, a broadband network, a telephonic network, anopen network, such as the Internet, or a private network, such as anintranet and/or the extranet, or any combination thereof. For example,the Internet can provide file transfer, remote log in, email, news, RSS,cloud-based services, instant messaging, visual voicemail, push mail,VoIP, and other services through any known or convenient protocol, suchas, but is not limited to the TCP/IP protocol, UDP, HTTP, DNS, FTP,UPnP, NSF, ISDN, PDH, RS-232, SDH, SONET, etc.

The networks 106 and/or 108 can be any collection of distinct networksoperating wholly or partially in conjunction to provide connectivity tothe client devices 150 and the host server 100 and may appear as one ormore networks to the serviced systems and devices. In one embodiment,communications to and from the client devices 150 can be achieved by, anopen network, such as the Internet, or a private network, broadbandnetwork, such as an intranet and/or the extranet. In one embodiment,communications can be achieved by a secure communications protocol, suchas secure sockets layer (SSL), or transport layer security (TLS).

In addition, communications can be achieved via one or more networks,such as, but are not limited to, one or more of WiMax, a Local AreaNetwork (LAN), Wireless Local Area Network (WLAN), a Personal areanetwork (PAN), a Campus area network (CAN), a Metropolitan area network(MAN), a Wide area network (WAN), a Wireless wide area network (WWAN),or any broadband network, and further enabled with technologies such as,by way of example, Global System for Mobile Communications (GSM),Personal Communications Service (PCS), Bluetooth, WiFi, Fixed WirelessData, 2G, 2.5G, 3G (e.g., WCDMA/UMTS based 3G networks), 4G,IMT-Advanced, pre-4G, LTE Advanced, mobile WiMax, WiMax 2,WirelessMAN-Advanced networks, enhanced data rates for GSM evolution(EDGE), General packet radio service (GPRS), enhanced GPRS, iBurst,UMTS, HSPDA, HSUPA, HSPA, HSPA+, UMTS-TDD, 1×RTT, EV-DO, messagingprotocols such as, TCP/IP, SMS, MMS, extensible messaging and presenceprotocol (XMPP), real time messaging protocol (RTMP), instant messagingand presence protocol (IMPP), instant messaging, USSD, IRC, or any otherwireless data networks, broadband networks, or messaging protocols.

With more detailed description below, and with particular reference toFIGS. 2A-2E and 3A-3E, one or more embodiments disclosed herein canprovide techniques to model the signaling that takes place in a mobilenetwork (e.g., network 106), to allocate signaling to one or morespecific applications (e.g., so as to determine which applications arecausing the traffic signals), and to model traffic signaling savingsresulted from the distributed caching and proxy system described herein(e.g., as implemented by client-side proxy 175 and/or server-side proxy125, FIG. 1C).

The present embodiments recognize that data signaling in the mobilenetwork takes place when, for example, radio connections get set up(e.g., connected) or torn down (e.g., disconnected). Moreover, eachradio connection can be used for one or more transactions/datatransfers, which can source from one or more applications.

To enable or enhance the performance of the data traffic and signaloptimization for the network, the present embodiments can include one ormore fields (e.g., expanded “log/reporting data” fields). The one ormore fields can be calculated by, for example, the client-side proxy 175and/or server-side proxy 125, to define and identify at least: (1)whether a transaction causes a connection (and thus correspondingsignaling); and (2) the number of connections that are reduced or savedby the disclosed embodiments of distributed caching and proxy system.

It is noted that, for convenience, a client (e.g., local proxy 105, 175,275) of the distributed caching system can be referred to herein as an“Network optimization client” or “network optimization client.”Similarly, a server (e.g., host server 111, 100, 300 hosting proxyserver 113, 125, 325) of the distributed caching system can be referredto herein as an “Network optimization server” or “network optimizationserver.” The client and/or server, individually or collectively, canimplement the distributed caching techniques described herein. Thedistributed caching techniques include, but are not limited to, theSignal Optimization and Extended Caching techniques referred to hereinas “Network optimization” or “network optimization.”

In one embodiment, a log/reporting data analysis core module can performcalculations and/or determinations for measurements and modeling of thesignals. The log/reporting data analysis core module, which can beincluded in client-side proxy 175 and/or server-side proxy 125 (e.g., asshown in FIGS. 2E and 3E), is described in more detail below.

FIG. 1A-1 depicts an example block diagram illustrating an architecturaloverview of a distributed Network optimization system including aNetwork optimization (network optimization) client (or local) proxy 175and a network optimization (or host) server 100 that are configured to,individually or in combination, model signaling in a mobile network asdescribed herein.

In one embodiment, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Thecrcs-analysis core tool or module can include hardware and/or softwaremodules and can be included in one or both of the network optimizationclient (local) proxy 175 and a network optimization (host) server 150.

In one embodiment, the expanded log/reporting data fields are calculatedin order to model the optimizations or savings of the Networkoptimization architecture (e.g., the mobile data traffic optimizationtechnology). For example, the expanded log/reporting data fields canmeasure an overall efficiency for the Network optimization architecture.The fields can be calculated by the network optimization client (local)proxy 175 and/or by the network optimization (host) server 150.Additionally, fields can be calculated per mobile device and/or fieldscan be calculated for modeling the signaling attributed to individualapplications executing on a mobile device. For example, the signalingcan be identified and allocated (or attributed) to specific applicationsto, for example, determine which applications are chatty, whichapplications are causing problematic signaling, etc.

The mobile device 150 can include any number of mobile deviceapplications. The applications can be built-in, pre-installed, ordownload by a user of the mobile device. Additionally, the applicationscan be in communication with (be handled by) the network optimizationclient proxy 175 or have a direct connection to the network (e.g.,Internet). As illustrated in the example of FIG. 1A-1, applications 1-3are shown each initiating transactions. Applications 1 and 2 are shownbeing handled by the network optimization client 175 while application 3is shown having a direct connection to the network (e.g., Internet).Applications 1 and 2 may also be referred to as “radio-aware” herein.Application 3 is not handled by Network optimization architecture, butnevertheless can cause radio up (i.e., mobile device radio connection).The radio connection can be tracked using a radio log. It is appreciatedthat each application can initiate any number of transactions that mayor may not cause network signaling.

As discussed above, expanded log/reporting data fields described hereincan be calculated in order to measure Network optimization solutionefficiency including signaling efficiency and time connected efficiency.For example, the signaling efficiency and time connected efficiency canbe calculated for the signaling associated with a mobile device. Thesignaling efficiency (also referred to as signaling savings) representsan amount of saved mobile network connections. Similarly, the timeconnected efficiency (also referred to as time savings) representsamount of saved mobile network up-time.

In one embodiment, the expanded log/reporting data fields can be dividedinto multiple types. For example, the expanded log/reporting data fieldscan include a connection flag type and a time connected counts type.Additionally, the expanded log/reporting data fields can be divided intoseveral categories as illustrated below in Table 1.

TABLE 1 log/reporting data fields categories Actual Simulated Simulatedper App Simulated per Host Actual A RS RSpA RSpH Virtual V VS VSpA VSpHSavings V − A VS − RS VSpA − RSpA VSpH − RSpH

As discussed above, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. In one embodiment, modeling the signaling ofthe mobile device includes making various connection and timecalculations. Examples of the various connection and time calculationsare discussed in greater detail with respect to FIGS. 10A-26N.

FIG. 1B illustrates an example diagram of a proxy and cache systemdistributed between the host server 100 and device 150 which facilitatesnetwork traffic management between the device 150 and an applicationserver or content provider 110, or other servers such as an ad server120A, promotional content server 120B, or an e-coupon server 120C forresource conservation and content caching. The proxy system distributedamong the host server 100 and the device 150 can further monitor mobileapplication activities for malicious traffic on a mobile device and/orautomatically generate and/or distribute policy information regardingmalicious traffic in a wireless network.

The distributed proxy and/or cache system (e.g., (distributed) trafficoptimizer, traffic management system, (distributed) content cachingmechanism for traffic alleviation) (e.g., (distributed) trafficoptimizer, traffic management system, (distributed) content cachingmechanism for traffic alleviation) can include, for example, the proxyserver 125 (e.g., remote proxy) and the server cache, 135 components onthe server side. The server-side proxy 125 and cache 135 can, asillustrated, reside internal to the host server 100. In addition, theproxy server 125 and cache 135 on the server-side can be partially orwholly external to the host server 100 and in communication via one ormore of the networks 106 and 108. For example, the proxy server 125 maybe external to the host server and the server cache 135 may bemaintained at the host server 100. Alternatively, the proxy server 125may be within the host server 100 while the server cache is external tothe host server 100. In addition, each of the proxy server 125 and thecache 135 may be partially internal to the host server 100 and partiallyexternal to the host server 100. The application server/content provider110 can by any server including third party servers or service/contentproviders further including advertisement, promotional content,publication, or electronic coupon servers or services. Similarly,separate advertisement servers 120A, promotional content servers 120B,and/or e-Coupon servers 120C as application servers or content providersare illustrated by way of example.

The distributed system can also, include, in one embodiment, client-sidecomponents, including by way of example but not limitation, a localproxy 175 (e.g., a mobile client on a mobile device) and/or a localcache 185, which can, as illustrated, reside internal to the device 150(e.g., a mobile device).

In addition, the client-side proxy 175 and local cache 185 can bepartially or wholly external to the device 150 and in communication viaone or more of the networks 106 and 108. For example, the local proxy175 may be external to the device 150 and the local cache 185 may bemaintained at the device 150. Alternatively, the local proxy 175 may bewithin the device 150 while the local cache 185 is external to thedevice 150. In addition, each of the proxy 175 and the cache 185 may bepartially internal to the host server 100 and partially external to thehost server 100.

In one embodiment, the distributed system can include an optionalcaching proxy server 199. The caching proxy server 199 can be acomponent which is operated by the application server/content provider110, the host server 100, or a network service provider 112, and or anycombination of the above to facilitate network traffic management fornetwork and device resource conservation. Proxy server 199 can be used,for example, for caching content to be provided to the device 150, forexample, from one or more of, the application server/provider 110, hostserver 100, and/or a network service provider 112. Content caching canalso be entirely or partially performed by the remote proxy 125 tosatisfy application requests or other data requests at the device 150.

In context aware traffic management and optimization for resourceconservation in a network (e.g., cellular or other wireless networks),characteristics of user activity/behavior and/or application behavior ata mobile device (e.g., any wireless device) 150 can be tracked by thelocal proxy 175 and communicated, over the network 106 to the proxyserver 125 component in the host server 100, for example, as connectionmetadata. The proxy server 125 which in turn is coupled to theapplication server/provider 110 provides content and data to satisfyrequests made at the device 150.

In addition, the local proxy 175 can identify and retrieve mobile deviceproperties, including one or more of, battery level, network that thedevice is registered on, radio state, or whether the mobile device isbeing used (e.g., interacted with by a user). In some instances, thelocal proxy 175 can delay, expedite (prefetch), and/or modify data priorto transmission to the proxy server 125, when appropriate, as will befurther detailed with references to the description associated with theexamples of FIG. 2-5.

The local database 185 can be included in the local proxy 175 or coupledto the local proxy 175 and can be queried for a locally stored responseto the data request prior to the data request being forwarded on to theproxy server 125. Locally cached responses can be used by the localproxy 175 to satisfy certain application requests of the mobile device150, by retrieving cached content stored in the cache storage 185, whenthe cached content is still valid.

Similarly, the proxy server 125 of the host server 100 can also delay,expedite, or modify data from the local proxy prior to transmission tothe content sources (e.g., the application server/content provider 110).In addition, the proxy server 125 uses device properties and connectionmetadata to generate rules for satisfying request of applications on themobile device 150. The proxy server 125 can gather real time trafficinformation about requests of applications for later use in optimizingsimilar connections with the mobile device 150 or other mobile devices.

In general, the local proxy 175 and the proxy server 125 are transparentto the multiple applications executing on the mobile device. The localproxy 175 is generally transparent to the operating system or platformof the mobile device and may or may not be specific to devicemanufacturers. In some instances, the local proxy 175 is optionallycustomizable in part or in whole to be device specific. In someembodiments, the local proxy 175 may be bundled into a wireless model, afirewall, and/or a router.

In one embodiment, the host server 100 can in some instances, utilizethe store and forward functions of a short message service center (SMSC)112, such as that provided by the network service provider, incommunicating with the device 150 in achieving network trafficmanagement. Note that 112 can also utilize any other type of alternativechannel including USSD or other network control mechanisms. The hostserver 100 can forward content or HTTP responses to the SMSC 112 suchthat it is automatically forwarded to the device 150 if available, andfor subsequent forwarding if the device 150 is not currently available.

In general, the disclosed distributed proxy and/or cache system (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation) (e.g.,(distributed) traffic optimizer, traffic management system,(distributed) content caching mechanism for traffic alleviation) allowsoptimization of network usage, for example, by serving requests from thelocal cache 185, the local proxy 175 reduces the number of requests thatneed to be satisfied over the network 106. Further, the local proxy 175and the proxy server 125 may filter irrelevant data from thecommunicated data. In addition, the local proxy 175 and the proxy server125 can also accumulate low priority data and send it in batches toavoid the protocol overhead of sending individual data fragments. Thelocal proxy 175 and the proxy server 125 can also compress or transcodethe traffic, reducing the amount of data sent over the network 106and/or 108. The signaling traffic in the network 106 and/or 108 can bereduced, as the networks are now used less often and the network trafficcan be synchronized among individual applications.

With respect to the battery life of the mobile device 150, by servingapplication or content requests from the local cache 185, the localproxy 175 can reduce the number of times the radio module is powered up.The local proxy 175 and the proxy server 125 can work in conjunction toaccumulate low priority data and send it in batches to reduce the numberof times and/or amount of time when the radio is powered up. The localproxy 175 can synchronize the network use by performing the batched datatransfer for all connections simultaneously.

FIG. 1C illustrates an example diagram of the logical architecture of adistributed proxy and cache system.

The distributed system can include, for example the followingcomponents:

Client Side Proxy 175: a component installed in the Smartphone, mobiledevice or wireless device 150 that interfaces with device's operatingsystem, as well as with data services and applications installed in thedevice. The client side proxy 175 is typically compliant with and ableto operate with standard or state of the art networking protocols.Additional components and features of the client-side proxy 175 areillustrated with further references to the examples of FIG. 2A-FIG. 2Band FIG. 4A-4C.

The server side proxy 125 can include one or more servers that caninterface with third party application servers (e.g., 199), mobileoperator's network (which can be proxy 199 or an additional server thatis not illustrated) and/or the client side proxy 175. In general, theserver side proxy 125 can be compliant with and is generally able tooperate with standard or state of the art networking protocols and/orspecifications for interacting with mobile network elements and/or thirdparty servers. Additional components and features of the server-sideproxy 125 are illustrated with further references to the examples ofFIG. 3A-FIG. 3B and FIG. 5A-5C.

Reporting and Usage Analytics Server 174: The Reporting and UsageAnalytics system or component 174 can collect information from theclient side 175 and/or the server side 125 and provides the necessarytools for producing reports and usage analytics can used for analyzingtraffic and signaling data. Such analytics can be used by the proxysystem in managing/reducing network traffic or by the network operatorin monitoring their networks for possible improvements and enhancements.Note that the reporting and usage analytics system/component 174 asillustrated, may be a server separate from the server-side proxy 125, orit may be a component of the server-side proxy 125, residing partiallyor wholly therein.

FIG. 1D illustrates an example diagram showing the architecture ofclient side components in a distributed proxy and cache system.

The client side components 175 can include software components or agentsinstalled on the mobile device that enables traffic optimization andperforms the related functionalities on the client side. Components ofthe client side proxy 175 can operate transparently for end users andapplications 163. The client side proxy 175 can be installed on mobiledevices for optimization to take place, and it can effectuate changes onthe data routes. Once data routing is modified, the client side proxy175 can respond to application requests to service providers or hostservers, in addition to or instead of letting those applications 163access data network directly. In general, applications 163 on the mobiledevice will not notice that the client side proxy 175 is responding totheir requests. Some example components of the client side proxy 175 aredescribed as follows:

Device State Monitor 121: The device state monitor 121 can beresponsible for identifying several states and metrics in the device,such as network status, display status, battery level, etc. such thatthe remaining components in the client side proxy 175 can operate andmake decisions according to device state, acting in an optimal way ineach state.

Traffic Recognizer 122: The traffic recognizer 122 analyzes all trafficbetween the wireless device applications 163 and their respective hostservers in order to identify recurrent patterns. Supported transportprotocols include, for example, DNS, HTTP and HTTPS, such that trafficthrough those ports is directed to the client side proxy 175. Whileanalyzing traffic, the client side proxy 175 can identify recurringpolling patterns which can be candidates to be performed remotely by theserver side proxy 125, and send to the protocol optimizer 123.

Protocol Optimizer 123: The protocol optimizer 123 can implement thelogic of serving recurrent request from the local cache 185 instead ofallowing those request go over the network to the serviceprovider/application host server. One is its tasks is to eliminate orminimize the need to send requests to the network, positively affectingnetwork congestion and device battery life.

Local Cache 185: The local cache 185 can store responses to recurrentrequests, and can be used by the Protocol Optimizer 123 to sendresponses to the applications 163.

Traffic Scheduler 124: The traffic scheduler 124 can temporally movecommunications to optimize usage of device resources by unifyingkeep-alive signaling so that some or all of the different applications163 can send keep-alive messages at the same time (traffic pipelining).Traffic scheduler 124 may also decide to delay transmission of data thatis not relevant at a given time (for example, when the device is notactively used).

Policy Manager 125: The policy manager 125 can store and enforce trafficoptimization and reporting policies provisioned by a Policy ManagementServer (PMS). At the client side proxy 175 first start, trafficoptimization and reporting policies (policy profiles) that is to beenforced in a particular device can be provisioned by the PolicyManagement Server.

Watch Dog 127: The watch dog 127 can monitor the client side proxy 175operating availability. In case the client side proxy 175 is not workingdue to a failure or because it has been disabled, the watchdog 127 canreset DNS routing rules information and can restore original DNSsettings for the device to continue working until the client side proxy175 service is restored.

Reporting Agent 126: The reporting agent 126 can gather informationabout the events taking place in the device and sends the information tothe Reporting Server. Event details are stored temporarily in the deviceand transferred to reporting server only when the data channel state isactive. If the client side proxy 175 doesn't send records withintwenty-four hours, the reporting agent 126 may attempt to open theconnection and send recorded entries or, in case there are no entries instorage, an empty reporting packet. All reporting settings areconfigured in the policy management server.

Push Client 128: The push client 128 can be responsible for the trafficto between the server side proxy 125 and the client side proxy 175. Thepush client 128 can send out service requests like content updaterequests and policy update requests, and receives updates to thoserequests from the server side proxy 125. In addition, push client 128can send data to a reporting server (e.g., the reporting and/or usageanalytics system which may be internal to or external to the server sideproxy 125).

The proxy server 199 has a wide variety of uses, from speeding up a webserver by caching repeated requests, to caching web, DNS and othernetwork lookups for a group of clients sharing network resources. Theproxy server 199 is optional. The distributed proxy and cache system(125 and/or 175) allows for a flexible proxy configuration using eitherthe proxy 199, additional proxy(s) in operator's network, or integratingboth proxies 199 and an operator's or other third-party's proxy.

FIG. 1E illustrates a diagram of the example components on the serverside of the distributed proxy and cache system.

The server side 125 of the distributed system can include, for example arelay server 142, which interacts with a traffic harmonizer 144, apolling server 145 and/or a policy management server 143. Each of thevarious components can communicate with the client side proxy 175, orother third party (e.g., application server/service provider 110 and/orother proxy 199) and/or a reporting and usage analytics system. Someexample components of the server side proxy 125 is described as follows:

Relay Server 142: The relay server 142 is the routing agent in thedistributed proxy architecture. The relay server 142 manages connectionsand communications with components on the client-side proxy 175installed on devices and provides an administrative interface forreports, provisioning, platform setup, and so on.

Notification Server 141: The notification server 141 is a module able toconnect to an operator's SMSC gateways and deliver SMS notifications tothe client-side proxy 175. SMS notifications can be used when an IP linkis not currently active, in order to avoid the client-side proxy 175from activating a connection over the wireless data channel, thusavoiding additional signaling traffic. However, if the IP connectionhappens to be open for some other traffic, the notification server 141can use it for sending the notifications to the client-side proxy 175.The user database can store operational data including endpoint(MSISDN), organization and Notification server 141 gateway for eachresource (URIs or URLs).

Traffic Harmonizer 144: The traffic harmonizer 144 can be responsiblefor communication between the client-side proxy 175 and the pollingserver 145. The traffic harmonizer 144 connects to the polling server145 directly or through the data storage 130, and to the client over anyopen or proprietary protocol such as the 7TP, implemented for trafficoptimization. The traffic harmonizer 144 can be also responsible fortraffic pipelining on the server side: if there's cached content in thedatabase for the same client, this can be sent over to the client in onemessage.

Polling Server 145: The polling server 145 can poll third partyapplication servers on behalf of applications that are being optimized).If a change occurs (i.e. new data available) for an application, thepolling server 145 can report to the traffic harmonizer 144 which inturn sends a notification message to the client-side proxy 175 for it toclear the cache and allow application to poll application serverdirectly.

Policy Management Server 143: The policy management server (PMS) 143allows administrators to configure and store policies for theclient-side proxies 175 (device clients). It also allows administratorsto notify the client-side proxies 175 about policy changes. Using thepolicy management server 143, each operator can configure the policiesto work in the most efficient way for the unique characteristics of eachparticular mobile operator's network.

Reporting and Usage Analytics Component: The Reporting and UsageAnalytics component or system collects information from the client side175 and/or from the server side 125, and provides the tools forproducing reports and usage analytics that operators can use foranalyzing application signaling and data consumption.

FIG. 1F illustrates an example diagram showing data flows betweenexample client side components in a distributed proxy and cache system.Traffic from applications (e.g., App1, App2, App3 to AppN), client sideproxy (e.g., local proxy) 175, IP Routing Tables (e.g., in the AndroidOperating System Layer), Network Access Layer and Wireless Network aredepicted.

In one implementation, non-optimized application traffic flow, such astraffic from App1, can completely bypass the client side proxy 175components and proceed directly through the operating system layer(e.g., the Android OS layer) and Network Access Layer to the wirelessnetwork. Traffic that that is not optimized can include, but is notlimited to: rich media, like video and audio, as well as traffic fromnetworks and applications that has been configured to bypassoptimization and traffic pending optimization, and the like. In oneembodiment, all traffic can be configured to bypass the clientside/server side proxy.

In another implementation, optimized application traffic, such astraffic from App2, can be redirected from the application to the clientside proxy 175. By default, this can be traffic on ports 80 (HTTP) and53 (DNS), and selected traffic on port 443 (HTTPS), for example.However, traffic to other ports can be configured to be directed to theclient side proxy.

In yet another implementation, traffic flow can be between the clientside proxy 175 and the origin servers (e.g., content server 110) via theInternet and/or between the client side proxy 175 and the server sideproxy (e.g., proxy server) 125.

FIG. 2A depicts a block diagram illustrating an example of client-sidecomponents in a distributed proxy and cache system residing on a device250 that manages traffic in a wireless network for resourceconservation, content caching, and/or traffic management. Theclient-side proxy (or local proxy 275) can further categorize mobiletraffic and/or implement delivery policies based on applicationbehavior, content priority, user activity, and/or user expectations.

The device 250, which can be a portable or mobile device (e.g., anywireless device), such as a portable phone, generally includes, forexample, a network interface 208 an operating system 204, a context API206, and mobile applications which may be proxy-unaware 210 orproxy-aware 220. Note that the device 250 is specifically illustrated inthe example of FIG. 2 as a mobile device, such is not a limitation andthat device 250 may be any wireless, broadband, portable/mobile ornon-portable device able to receive, transmit signals to satisfy datarequests over a network including wired or wireless networks (e.g.,WiFi, cellular, Bluetooth, LAN, WAN, etc.).

The network interface 208 can be a networking module that enables thedevice 250 to mediate data in a network with an entity that is externalto the host server 250, through any known and/or convenientcommunications protocol supported by the host and the external entity.The network interface 208 can include one or more of a network adaptorcard, a wireless network interface card (e.g., SMS interface, WiFiinterface, interfaces for various generations of mobile communicationstandards including but not limited to 2G, 3G, 3.5G, 4G, LTE, etc.),Bluetooth, or whether or not the connection is via a router, an accesspoint, a wireless router, a switch, a multilayer switch, a protocolconverter, a gateway, a bridge, a bridge router, a hub, a digital mediareceiver, and/or a repeater.

Device 250 can further include, client-side components of thedistributed proxy and cache system which can include, a local proxy 275(e.g., a mobile client of a mobile device) and a cache 285. In oneembodiment, the local proxy 275 includes a user activity module 215, aproxy API 225, a request/transaction manager 235, a caching policymanager 245 having an application protocol module 248, a traffic shapingengine 255, and/or a connection manager 265. The traffic shaping engine255 may further include an alignment module 256 and/or a batching module257, the connection manager 265 may further include a radio controller266. The request/transaction manager 235 can further include anapplication behavior detector 236 and/or a prioritization engine 241,the application behavior detector 236 may further include a patterndetector 237 and/or and application profile generator 239. Additional orless components/modules/engines can be included in the local proxy 275and each illustrated component.

As used herein, a “module,” “a manager,” a “handler,” a “detector,” an“interface,” a “controller,” a “normalizer,” a “generator,” an“invalidator,” or an “engine” includes a general purpose, dedicated orshared processor and, typically, firmware or software modules that areexecuted by the processor. Depending upon implementation-specific orother considerations, the module, manager, handler, detector, interface,controller, normalizer, generator, invalidator, or engine can becentralized or its functionality distributed. The module, manager,handler, detector, interface, controller, normalizer, generator,invalidator, or engine can include general or special purpose hardware,firmware, or software embodied in a computer-readable (storage) mediumfor execution by the processor.

As used herein, a computer-readable medium or computer-readable storagemedium is intended to include all mediums that are statutory (e.g., inthe United States, under 35 U.S.C. 101), and to specifically exclude allmediums that are non-statutory in nature to the extent that theexclusion is necessary for a claim that includes the computer-readable(storage) medium to be valid. Known statutory computer-readable mediumsinclude hardware (e.g., registers, random access memory (RAM),non-volatile (NV) storage, to name a few), but may or may not be limitedto hardware.

In one embodiment, a portion of the distributed proxy and cache systemfor network traffic management resides in or is in communication withdevice 250, including local proxy 275 (mobile client) and/or cache 285.The local proxy 275 can provide an interface on the device 250 for usersto access device applications and services including email, IM, voicemail, visual voicemail, feeds, Internet, games, productivity tools, orother applications, etc.

The proxy 275 is generally application independent and can be used byapplications (e.g., both proxy-aware and proxy-unaware applications 210and 220 or mobile applications) to open TCP connections to a remoteserver (e.g., the server 100 in the examples of FIG. 1A-1B and/or serverproxy 125/325 shown in the examples of FIG. 1B and FIG. 3A). In someinstances, the local proxy 275 includes a proxy API 225 which can beoptionally used to interface with proxy-aware applications 220 (orapplications (e.g., mobile applications) on a mobile device (e.g., anywireless device)).

The applications 210 and 220 can generally include any user application,widgets, software, HTTP-based application, web browsers, video or othermultimedia streaming or downloading application, video games, socialnetwork applications, email clients, RSS management applications,application stores, document management applications, productivityenhancement applications, etc. The applications can be provided with thedevice OS, by the device manufacturer, by the network service provider,downloaded by the user, or provided by others.

One embodiment of the local proxy 275 includes or is coupled to acontext API 206, as shown. The context API 206 may be a part of theoperating system 204 or device platform or independent of the operatingsystem 204, as illustrated. The operating system 204 can include anyoperating system including but not limited to, any previous, current,and/or future versions/releases of, Windows Mobile, iOS, Android,Symbian, Palm OS, Brew MP, Java 2 Micro Edition (J2ME), Blackberry, etc.

The context API 206 may be a plug-in to the operating system 204 or aparticular client/application on the device 250. The context API 206 candetect signals indicative of user or device activity, for example,sensing motion, gesture, device location, changes in device location,device backlight, keystrokes, clicks, activated touch screen, mouseclick or detection of other pointer devices. The context API 206 can becoupled to input devices or sensors on the device 250 to identify thesesignals. Such signals can generally include input received in responseto explicit user input at an input device/mechanism at the device 250and/or collected from ambient signals/contextual cues detected at or inthe vicinity of the device 250 (e.g., light, motion, piezoelectric,etc.).

In one embodiment, the user activity module 215 interacts with thecontext API 206 to identify, determine, infer, detect, compute, predict,and/or anticipate, characteristics of user activity on the device 250.Various inputs collected by the context API 206 can be aggregated by theuser activity module 215 to generate a profile for characteristics ofuser activity. Such a profile can be generated by the user activitymodule 215 with various temporal characteristics. For instance, useractivity profile can be generated in real-time for a given instant toprovide a view of what the user is doing or not doing at a given time(e.g., defined by a time window, in the last minute, in the last 30seconds, etc.), a user activity profile can also be generated for a‘session’ defined by an application or web page that describes thecharacteristics of user behavior with respect to a specific task theyare engaged in on the device 250, or for a specific time period (e.g.,for the last 2 hours, for the last 5 hours).

Additionally, characteristic profiles can be generated by the useractivity module 215 to depict a historical trend for user activity andbehavior (e.g., 1 week, 1 mo., 2 mo., etc.). Such historical profilescan also be used to deduce trends of user behavior, for example, accessfrequency at different times of day, trends for certain days of the week(weekends or week days), user activity trends based on location data(e.g., IP address, GPS, or cell tower coordinate data) or changes inlocation data (e.g., user activity based on user location, or useractivity based on whether the user is on the go, or traveling outside ahome region, etc.) to obtain user activity characteristics.

In one embodiment, user activity module 215 can detect and track useractivity with respect to applications, documents, files, windows, icons,and folders on the device 250. For example, the user activity module 215can detect when an application or window (e.g., a web browser or anyother type of application) has been exited, closed, minimized,maximized, opened, moved into the foreground, or into the background,multimedia content playback, etc.

In one embodiment, characteristics of the user activity on the device250 can be used to locally adjust behavior of the device (e.g., mobiledevice or any wireless device) to optimize its resource consumption suchas battery/power consumption and more generally, consumption of otherdevice resources including memory, storage, and processing power. In oneembodiment, the use of a radio on a device can be adjusted based oncharacteristics of user behavior (e.g., by the radio controller 266 ofthe connection manager 265) coupled to the user activity module 215. Forexample, the radio controller 266 can turn the radio on or off, based oncharacteristics of the user activity on the device 250. In addition, theradio controller 266 can adjust the power mode of the radio (e.g., to bein a higher power mode or lower power mode) depending on characteristicsof user activity.

In one embodiment, characteristics of the user activity on device 250can also be used to cause another device (e.g., other computers, amobile device, a wireless device, or a non-portable device) or server(e.g., host server 100 and 300 in the examples of FIG. 1A-B and FIG. 3A)which can communicate (e.g., via a cellular or other network) with thedevice 250 to modify its communication frequency with the device 250.The local proxy 275 can use the characteristics information of userbehavior determined by the user activity module 215 to instruct theremote device as to how to modulate its communication frequency (e.g.,decreasing communication frequency, such as data push frequency if theuser is idle, requesting that the remote device notify the device 250 ifnew data, changed, data, or data of a certain level of importancebecomes available, etc.).

In one embodiment, the user activity module 215 can, in response todetermining that user activity characteristics indicate that a user isactive after a period of inactivity, request that a remote device (e.g.,server host server 100 and 300 in the examples of FIG. 1A-B and FIG. 3A)send the data that was buffered as a result of the previously decreasedcommunication frequency.

In addition, or in alternative, the local proxy 275 can communicate thecharacteristics of user activity at the device 250 to the remote device(e.g., host server 100 and 300 in the examples of FIG. 1A-B and FIG. 3A)and the remote device determines how to alter its own communicationfrequency with the device 250 for network resource conservation andconservation of device 250 resources.

One embodiment of the local proxy 275 further includes arequest/transaction manager 235, which can detect, identify, intercept,process, manage, data requests initiated on the device 250, for example,by applications 210 and/or 220, and/or directly/indirectly by a userrequest. The request/transaction manager 235 can determine how and whento process a given request or transaction, or a set ofrequests/transactions, based on transaction characteristics.

The request/transaction manager 235 can prioritize requests ortransactions made by applications and/or users at the device 250, forexample by the prioritization engine 241. Importance or priority ofrequests/transactions can be determined by the request/transactionmanager 235 by applying a rule set, for example, according to timesensitivity of the transaction, time sensitivity of the content in thetransaction, time criticality of the transaction, time criticality ofthe data transmitted in the transaction, and/or time criticality orimportance of an application making the request.

In addition, transaction characteristics can also depend on whether thetransaction was a result of user-interaction or other user-initiatedaction on the device (e.g., user interaction with a application (e.g., amobile application)). In general, a time critical transaction caninclude a transaction resulting from a user-initiated data transfer, andcan be prioritized as such. Transaction characteristics can also dependon the amount of data that will be transferred or is anticipated to betransferred as a result of the requested transaction. For example, theconnection manager 265, can adjust the radio mode (e.g., high power orlow power mode via the radio controller 266) based on the amount of datathat will need to be transferred.

In addition, the radio controller 266/connection manager 265 can adjustthe radio power mode (high or low) based on time criticality/sensitivityof the transaction. The radio controller 266 can trigger the use of highpower radio mode when a time-critical transaction (e.g., a transactionresulting from a user-initiated data transfer, an application running inthe foreground, any other event meeting a certain criteria) is initiatedor detected.

In general, the priorities can be set by default, for example, based ondevice platform, device manufacturer, operating system, etc. Prioritiescan alternatively or in additionally be set by the particularapplication; for example, the Facebook application (e.g., a mobileapplication) can set its own priorities for various transactions (e.g.,a status update can be of higher priority than an add friend request ora poke request, a message send request can be of higher priority than amessage delete request, for example), an email client or IM chat clientmay have its own configurations for priority. The prioritization engine241 may include set of rules for assigning priority.

The prioritization engine 241 can also track network providerlimitations or specifications on application or transaction priority indetermining an overall priority status for a request/transaction.Furthermore, priority can in part or in whole be determined by userpreferences, either explicit or implicit. A user, can in general, setpriorities at different tiers, such as, specific priorities forsessions, or types, or applications (e.g., a browsing session, a gamingsession, versus an IM chat session, the user may set a gaming session toalways have higher priority than an IM chat session, which may havehigher priority than web-browsing session). A user can setapplication-specific priorities, (e.g., a user may set Facebook-relatedtransactions to have a higher priority than LinkedIn-relatedtransactions), for specific transaction types (e.g., for all sendmessage requests across all applications to have higher priority thanmessage delete requests, for all calendar-related events to have a highpriority, etc.), and/or for specific folders.

The prioritization engine 241 can track and resolve conflicts inpriorities set by different entities. For example, manual settingsspecified by the user may take precedence over device OS settings,network provider parameters/limitations (e.g., set in default for anetwork service area, geographic locale, set for a specific time of day,or set based on service/fee type) may limit any user-specified settingsand/or application-set priorities. In some instances, a manualsynchronization request received from a user can override some, most, orall priority settings in that the requested synchronization is performedwhen requested, regardless of the individually assigned priority or anoverall priority ranking for the requested action.

Priority can be specified and tracked internally in any known and/orconvenient manner, including but not limited to, a binaryrepresentation, a multi-valued representation, a graded representationand all are considered to be within the scope of the disclosedtechnology.

TABLE 2 Change Change (initiated on device) Priority (initiated onserver) Priority Send email High Receive email High Delete email LowEdit email Often not possible to sync (Low if possible) (Un)read emailLow Move message Low New email in deleted items Low Read more HighDownload attachment High Delete an email Low (Un)Read an email Low NewCalendar event High Move messages Low Edit/change Calendar event HighAny calendar change High Any contact change High Add a contact HighWipe/lock device High Edit a contact High Settings change High Searchcontacts High Any folder change High Change a setting High Connectorrestart High (if no changes nothing is sent) Manual send/receive High IMstatus change Medium Social Network Status Updates Medium Auction outbidor change High Sever Weather Alerts High notification Weather UpdatesLow News Updates Low

Table 2 above shows, for illustration purposes, some examples oftransactions with examples of assigned priorities in a binaryrepresentation scheme. Additional assignments are possible foradditional types of events, requests, transactions, and as previouslydescribed, priority assignments can be made at more or less granularlevels, e.g., at the session level or at the application level, etc.

As shown by way of example in the above table 2, in general, lowerpriority requests/transactions can include, updating message status asbeing read, unread, deleting of messages, deletion of contacts; higherpriority requests/transactions, can in some instances include, statusupdates, new IM chat message, new email, calendar eventupdate/cancellation/deletion, an event in a mobile gaming session, orother entertainment related events, a purchase confirmation through aweb purchase or online, request to load additional or download content,contact book related events, a transaction to change a device setting,location-aware or location-based events/transactions, or any otherevents/request/transactions initiated by a user or where the user isknown to be, expected to be, or suspected to be waiting for a response,etc.

Inbox pruning events (e.g., email, or any other types of messages), aregenerally considered low priority and absent other impending events,generally will not trigger use of the radio on the device 250.Specifically, pruning events to remove old email or other content can be‘piggy backed’ with other communications if the radio is not otherwiseon, at the time of a scheduled pruning event. For example, if the userhas preferences set to ‘keep messages for 7 days old,’ then instead ofpowering on the device radio to initiate a message delete from thedevice 250 the moment that the message has exceeded 7 days old, themessage is deleted when the radio is powered on next. If the radio isalready on, then pruning may occur as regularly scheduled.

The request/transaction manager 235, can use the priorities for requests(e.g., by the prioritization engine 241) to manage outgoing traffic fromthe device 250 for resource optimization (e.g., to utilize the deviceradio more efficiently for battery conservation). For example,transactions/requests below a certain priority ranking may not triggeruse of the radio on the device 250 if the radio is not already switchedon, as controlled by the connection manager 265. In contrast, the radiocontroller 266 can turn on the radio such a request can be sent when arequest for a transaction is detected to be over a certain prioritylevel.

In one embodiment, priority assignments (such as that determined by thelocal proxy 275 or another device/entity) can be used cause a remotedevice to modify its communication with the frequency with the mobiledevice or wireless device. For example, the remote device can beconfigured to send notifications to the device 250 when data of higherimportance is available to be sent to the mobile device or wirelessdevice.

In one embodiment, transaction priority can be used in conjunction withcharacteristics of user activity in shaping or managing traffic, forexample, by the traffic shaping engine 255. For example, the trafficshaping engine 255 can, in response to detecting that a user is dormantor inactive, wait to send low priority transactions from the device 250,for a period of time. In addition, the traffic shaping engine 255 canallow multiple low priority transactions to accumulate for batchtransferring from the device 250 (e.g., via the batching module 257). Inone embodiment, the priorities can be set, configured, or readjusted bya user. For example, content depicted in Table 2 in the same or similarform can be accessible in a user interface on the device 250 and forexample, used by the user to adjust or view the priorities.

The batching module 257 can initiate batch transfer based on certaincriteria. For example, batch transfer (e.g., of multiple occurrences ofevents, some of which occurred at different instances in time) may occurafter a certain number of low priority events have been detected, orafter an amount of time elapsed after the first of the low priorityevent was initiated. In addition, the batching module 257 can initiatebatch transfer of the cumulated low priority events when a higherpriority event is initiated or detected at the device 250. Batchtransfer can otherwise be initiated when radio use is triggered foranother reason (e.g., to receive data from a remote device such as hostserver 100 or 300). In one embodiment, an impending pruning event(pruning of an inbox), or any other low priority events, can be executedwhen a batch transfer occurs.

In general, the batching capability can be disabled or enabled at theevent/transaction level, application level, or session level, based onany one or combination of the following: user configuration, devicelimitations/settings, manufacturer specification, network providerparameters/limitations, platform-specific limitations/settings, deviceOS settings, etc. In one embodiment, batch transfer can be initiatedwhen an application/window/file is closed out, exited, or moved into thebackground; users can optionally be prompted before initiating a batchtransfer; users can also manually trigger batch transfers.

In one embodiment, the local proxy 275 locally adjusts radio use on thedevice 250 by caching data in the cache 285. When requests ortransactions from the device 250 can be satisfied by content stored inthe cache 285, the radio controller 266 need not activate the radio tosend the request to a remote entity (e.g., the host server 100, 300, asshown in FIG. 1A and FIG. 3A or a content provider/application serversuch as the server/provider 110 shown in the examples of FIG. 1A andFIG. 1B). As such, the local proxy 275 can use the local cache 285 andthe cache policy manager 245 to locally store data for satisfying datarequests to eliminate or reduce the use of the device radio forconservation of network resources and device battery consumption.

In leveraging the local cache, once the request/transaction manager 225intercepts a data request by an application on the device 250, the localrepository 285 can be queried to determine if there is any locallystored response, and also determine whether the response is valid. Whena valid response is available in the local cache 285, the response canbe provided to the application on the device 250 without the device 250needing to access the cellular network or wireless broadband network.

If a valid response is not available, the local proxy 275 can query aremote proxy (e.g., the server proxy 325 of FIG. 3A) to determinewhether a remotely stored response is valid. If so, the remotely storedresponse (e.g., which may be stored on the server cache 135 or optionalcaching server 199 shown in the example of FIG. 1B) can be provided tothe mobile device, possibly without the mobile device 250 needing toaccess the cellular network, thus relieving consumption of networkresources.

If a valid cache response is not available, or if cache responses areunavailable for the intercepted data request, the local proxy 275, forexample, the caching policy manager 245, can send the data request to aremote proxy (e.g., server proxy 325 of FIG. 3A) which forwards the datarequest to a content source (e.g., application server/content provider110 of FIG. 1A) and a response from the content source can be providedthrough the remote proxy, as will be further described in thedescription associated with the example host server 300 of FIG. 3A. Thecache policy manager 245 can manage or process requests that use avariety of protocols, including but not limited to HTTP, HTTPS, IMAP,POP, SMTP, XMPP, and/or ActiveSync. The caching policy manager 245 canlocally store responses for data requests in the local database 285 ascache entries, for subsequent use in satisfying same or similar datarequests.

The caching policy manager 245 can request that the remote proxy monitorresponses for the data request and the remote proxy can notify thedevice 250 when an unexpected response to the data request is detected.In such an event, the cache policy manager 245 can erase or replace thelocally stored response(s) on the device 250 when notified of theunexpected response (e.g., new data, changed data, additional data,etc.) to the data request. In one embodiment, the caching policy manager245 is able to detect or identify the protocol used for a specificrequest, including but not limited to HTTP, HTTPS, IMAP, POP, SMTP,XMPP, and/or ActiveSync. In one embodiment, application specifichandlers (e.g., via the application protocol module 246 of the cachingpolicy manager 245) on the local proxy 275 allows for optimization ofany protocol that can be port mapped to a handler in the distributedproxy (e.g., port mapped on the proxy server 325 in the example of FIG.3A).

In one embodiment, the local proxy 275 notifies the remote proxy suchthat the remote proxy can monitor responses received for the datarequest from the content source for changed results prior to returningthe result to the device 250, for example, when the data request to thecontent source has yielded same results to be returned to the mobiledevice. In general, the local proxy 275 can simulate application serverresponses for applications on the device 250, using locally cachedcontent. This can prevent utilization of the cellular network fortransactions where new/changed data is not available, thus freeing upnetwork resources and preventing network congestion.

In one embodiment, the local proxy 275 includes an application behaviordetector 236 to track, detect, observe, monitor, applications (e.g.,proxy-aware and/or unaware applications 210 and 220) accessed orinstalled on the device 250. Application behaviors, or patterns indetected behaviors (e.g., via the pattern detector 237) of one or moreapplications accessed on the device 250 can be used by the local proxy275 to optimize traffic in a wireless network needed to satisfy the dataneeds of these applications.

For example, based on detected behavior of multiple applications, thetraffic shaping engine 255 can align content requests made by at leastsome of the applications over the network (wireless network) (e.g., viathe alignment module 256). The alignment module 256 can delay orexpedite some earlier received requests to achieve alignment. Whenrequests are aligned, the traffic shaping engine 255 can utilize theconnection manager to poll over the network to satisfy application datarequests. Content requests for multiple applications can be alignedbased on behavior patterns or rules/settings including, for example,content types requested by the multiple applications (audio, video,text, etc.), device (e.g., mobile or wireless device) parameters, and/ornetwork parameters/traffic conditions, network service providerconstraints/specifications, etc.

In one embodiment, the pattern detector 237 can detect recurrences inapplication requests made by the multiple applications, for example, bytracking patterns in application behavior. A tracked pattern caninclude, detecting that certain applications, as a background process,poll an application server regularly, at certain times of day, oncertain days of the week, periodically in a predictable fashion, with acertain frequency, with a certain frequency in response to a certaintype of event, in response to a certain type user query, frequency thatrequested content is the same, frequency with which a same request ismade, interval between requests, applications making a request, or anycombination of the above, for example.

Such recurrences can be used by traffic shaping engine 255 to offloadpolling of content from a content source (e.g., from an applicationserver/content provider 110 of FIG. 1A) that would result from theapplication requests that would be performed at the mobile device orwireless device 250 to be performed instead, by a proxy server (e.g.,proxy server 125 of FIG. 1B or proxy server 325 of FIG. 3A) remote fromthe device 250. Traffic shaping engine 255 can decide to offload thepolling when the recurrences match a rule. For example, there aremultiple occurrences or requests for the same resource that have exactlythe same content, or returned value, or based on detection of repeatabletime periods between requests and responses such as a resource that isrequested at specific times during the day. The offloading of thepolling can decrease the amount of bandwidth consumption needed by themobile device 250 to establish a wireless (cellular or other wirelessbroadband) connection with the content source for repetitive contentpolls.

As a result of the offloading of the polling, locally cached contentstored in the local cache 285 can be provided to satisfy data requestsat the device 250, when content change is not detected in the polling ofthe content sources. As such, when data has not changed, applicationdata needs can be satisfied without needing to enable radio use oroccupying cellular bandwidth in a wireless network. When data haschanged and/or new data has been received, the remote entity to whichpolling is offloaded, can notify the device 250. The remote entity maybe the host server 300 as shown in the example of FIG. 3A.

In one embodiment, the local proxy 275 can mitigate the need/use ofperiodic keep-alive messages (heartbeat messages) to maintain TCP/IPconnections, which can consume significant amounts of power thus havingdetrimental impacts on mobile device battery life. The connectionmanager 265 in the local proxy (e.g., the heartbeat manager 267) candetect, identify, and intercept any or all heartbeat (keep-alive)messages being sent from applications.

The heartbeat manager 267 can prevent any or all of these heartbeatmessages from being sent over the cellular, or other network, andinstead rely on the server component of the distributed proxy system(e.g., shown in FIG. 1B) to generate the and send the heartbeat messagesto maintain a connection with the backend (e.g., applicationserver/provider 110 in the example of FIG. 1A).

The local proxy 275 generally represents any one or a portion of thefunctions described for the individual managers, modules, and/orengines. The local proxy 275 and device 250 can include additional orless components; more or less functions can be included, in whole or inpart, without deviating from the novel art of the disclosure.

FIG. 2B depicts a block diagram illustrating a further example ofcomponents in the cache system shown in the example of FIG. 2A which iscapable of caching and adapting caching strategies for mobileapplication behavior and/or network conditions.

In one embodiment, the caching policy manager 245 includes a metadatagenerator 203, a cache look-up engine 205, a cache appropriatenessdecision engine 246, a poll schedule generator 247, an applicationprotocol module 248, a cache or connect selection engine 249 and/or alocal cache invalidator 244. The cache appropriateness decision engine246 can further include a timing predictor 246 a,a content predictor 246b, a request analyzer 246 c, and/or a response analyzer 246 d, and thecache or connect selection engine 249 includes a response scheduler 249a. The metadata generator 203 and/or the cache look-up engine 205 arecoupled to the cache 285 (or local cache) for modification or additionto cache entries or querying thereof.

The cache look-up engine 205 may further include an ID or URI filter 205a, the local cache invalidator 244 may further include a TTL manager 244a, and the poll schedule generator 247 may further include a scheduleupdate engine 247 a and/or a time adjustment engine 247 b. Oneembodiment of caching policy manager 245 includes an application cachepolicy repository 243. In one embodiment, the application behaviordetector 236 includes a pattern detector 237, a poll interval detector238, an application profile generator 239, and/or a priority engine 241.The poll interval detector 238 may further include a long poll detector238 a having a response/request tracking engine 238 b. The poll intervaldetector 238 may further include a long poll hunting detector 238 c. Theapplication profile generator 239 can further include a response delayinterval tracker 239 a.

The pattern detector 237, application profile generator 239, and thepriority engine 241 were also described in association with thedescription of the pattern detector shown in the example of FIG. 2A. Oneembodiment further includes an application profile repository 242 whichcan be used by the local proxy 275 to store information or metadataregarding application profiles (e.g., behavior, patterns, type of HTTPrequests, etc.)

The cache appropriateness decision engine 246 can detect, assess, ordetermine whether content from a content source (e.g., applicationserver/content provider 110 in the example of FIG. 1B) with which amobile device 250 interacts and has content that may be suitable forcaching. For example, the decision engine 246 can use information abouta request and/or a response received for the request initiated at themobile device 250 to determine cacheability, potential cacheability, ornon-cacheability. In some instances, the decision engine 246 caninitially verify whether a request is directed to a blacklisteddestination or whether the request itself originates from a blacklistedclient or application. If so, additional processing and analysis may notbe performed by the decision engine 246 and the request may be allowedto be sent over the air to the server to satisfy the request. The blacklisted destinations or applications/clients (e.g., mobile applications)can be maintained locally in the local proxy (e.g., in the applicationprofile repository 242) or remotely (e.g., in the proxy server 325 oranother entity).

In one embodiment, the decision engine 246, for example, via the requestanalyzer 246 c, collects information about an application or clientrequest generated at the mobile device 250. The request information caninclude request characteristics information including, for example,request method. For example, the request method can indicate the type ofHTTP request generated by the mobile application or client. In oneembodiment, response to a request can be identified as cacheable orpotentially cacheable if the request method is a GET request or POSTrequest. Other types of requests (e.g., OPTIONS, HEAD, PUT, DELETE,TRACE, or CONNECT) may or may not be cached. In general, HTTP requestswith uncacheable request methods will not be cached.

Request characteristics information can further include informationregarding request size, for example. Responses to requests (e.g., HTTPrequests) with body size exceeding a certain size will not be cached.For example, cacheability can be determined if the information about therequest indicates that a request body size of the request does notexceed a certain size. In some instances, the maximum cacheable requestbody size can be set to 8092 bytes. In other instances, different valuesmay be used, dependent on network capacity or network operator specificsettings, for example.

In some instances, content from a given application server/contentprovider (e.g., the server/content provider 110 of FIG. 1B) isdetermined to be suitable for caching based on a set of criteria, forexample, criteria specifying time criticality of the content that isbeing requested from the content source. In one embodiment, the localproxy (e.g., the local proxy 175 or 275 of FIG. 1B and FIG. 2A) appliesa selection criteria to store the content from the host server which isrequested by an application as cached elements in a local cache on themobile device to satisfy subsequent requests made by the application.

The cache appropriateness decision engine 246, further based on detectedpatterns of requests sent from the mobile device 250 (e.g., by a mobileapplication or other types of clients on the device 250) and/or patternsof received responses, can detect predictability in requests and/orresponses. For example, the request characteristics informationcollected by the decision engine 246, (e.g., the request analyzer 246 c)can further include periodicity information between a request and otherrequests generated by a same client on the mobile device or otherrequests directed to the same host (e.g., with similar or sameidentifier parameters).

Periodicity can be detected, by the decision engine 246 or the requestanalyzer 246 c, when the request and the other requests generated by thesame client occur at a fixed rate or nearly fixed rate, or at a dynamicrate with some identifiable or partially or wholly reproducible changingpattern. If the requests are made with some identifiable pattern (e.g.,regular intervals, intervals having a detectable pattern, or trend(e.g., increasing, decreasing, constant, etc.) the timing predictor 246a can determine that the requests made by a given application on adevice is predictable and identify it to be potentially appropriate forcaching, at least from a timing standpoint.

An identifiable pattern or trend can generally include any applicationor client behavior which may be simulated either locally, for example,on the local proxy 275 on the mobile device 250 or simulated remotely,for example, by the proxy server 325 on the host 300, or a combinationof local and remote simulation to emulate application behavior.

In one embodiment, the decision engine 246, for example, via theresponse analyzer 246 d, can collect information about a response to anapplication or client request generated at the mobile device 250. Theresponse is typically received from a server or the host of theapplication (e.g., mobile application) or client which sent the requestat the mobile device 250. In some instances, the mobile client orapplication can be the mobile version of an application (e.g., socialnetworking, search, travel management, voicemail, contact manager,email) or a web site accessed via a web browser or via a desktop client.

For example, response characteristics information can include anindication of whether transfer encoding or chunked transfer encoding isused in sending the response. In some instances, responses to HTTPrequests with transfer encoding or chunked transfer encoding are notcached, and therefore are also removed from further analysis. Therationale here is that chunked responses are usually large andnon-optimal for caching, since the processing of these transactions maylikely slow down the overall performance. Therefore, in one embodiment,cacheability or potential for cacheability can be determined whentransfer encoding is not used in sending the response.

In addition, the response characteristics information can include anassociated status code of the response which can be identified by theresponse analyzer 246 d. In some instances, HTTP responses withuncacheable status codes are typically not cached. The response analyzer246 d can extract the status code from the response and determinewhether it matches a status code which is cacheable or uncacheable. Somecacheable status codes include by way of example: 200—OK, 301—Redirect,302—Found, 303—See other, 304—Not Modified, 307 Temporary Redirect, or500—Internal server error. Some uncacheable status codes can include,for example, 403—Forbidden or 404—Not found.

In one embodiment, cacheability or potential for cacheability can bedetermined if the information about the response does not indicate anuncacheable status code or indicates a cacheable status code. If theresponse analyzer 246 d detects an uncacheable status code associatedwith a given response, the specific transaction (request/response pair)may be eliminated from further processing and determined to beuncacheable on a temporary basis, a semi-permanent, or a permanentbasis. If the status code indicates cacheability, the transaction (e.g.,request and/or response pair) may be subject to further processing andanalysis to confirm cacheability.

Response characteristics information can also include response sizeinformation. In general, responses can be cached locally at the mobiledevice 250 if the responses do not exceed a certain size. In someinstances, the default maximum cached response size is set to 128 KB. Inother instances, the max cacheable response size may be different and/ordynamically adjusted based on operating conditions, network conditions,network capacity, user preferences, network operator requirements, orother application-specific, user specific, and/or device-specificreasons. In one embodiment, the response analyzer 246 d can identify thesize of the response, and cacheability or potential for cacheability canbe determined if a given threshold or max value is not exceeded by theresponse size.

Furthermore, response characteristics information can include responsebody information for the response to the request and other response toother requests generated by a same client on the mobile device, ordirected to a same content host or application server. The response bodyinformation for the response and the other responses can be compared,for example, by the response analyzer 246 d, to prevent the caching ofdynamic content (or responses with content that changes frequently andcannot be efficiently served with cache entries, such as financial data,stock quotes, news feeds, real-time sporting event activities, etc.),such as content that would no longer be relevant or up-to-date if servedfrom cached entries.

The cache appropriateness decision engine 246 (e.g., the contentpredictor 246 b) can definitively identify repeatability or identifyindications of repeatability, potential repeatability, or predictabilityin responses received from a content source (e.g., the contenthost/application server 110 shown in the example of FIG. 1A-B).Repeatability can be detected by, for example, tracking at least tworesponses received from the content source and determines if the tworesponses are the same. For example, cacheability can be determined, bythe response analyzer 246 d, if the response body information for theresponse and the other responses sent by the same mobile client ordirected to the same host/server are same or substantially the same. Thetwo responses may or may not be responses sent in response toconsecutive requests. In one embodiment, hash values of the responsesreceived for requests from a given application are used to determinerepeatability of content (with or without heuristics) for theapplication in general and/or for the specific request. Additional sameresponses may be required for some applications or under certaincircumstances.

Repeatability in received content need not be 100% ascertained. Forexample, responses can be determined to be repeatable if a certainnumber or a certain percentage of responses are the same, or similar.The certain number or certain percentage of same/similar responses canbe tracked over a select period of time, set by default or set based onthe application generating the requests (e.g., whether the applicationis highly dynamic with constant updates or less dynamic with infrequentupdates). Any indicated predictability or repeatability, or possiblerepeatability, can be utilized by the distributed system in cachingcontent to be provided to a requesting application or client on themobile device 250.

In one embodiment, for a long poll type request, the local proxy 175 canbegin to cache responses on a third request when the response delaytimes for the first two responses are the same, substantially the same,or detected to be increasing in intervals. In general, the receivedresponses for the first two responses should be the same, and uponverifying that the third response received for the third request is thesame (e.g., if R0=R1=R2), the third response can be locally cached onthe mobile device. Less or more same responses may be required to begincaching, depending on the type of application, type of data, type ofcontent, user preferences, or carrier/network operator specifications.

Increasing response delays with same responses for long polls canindicate a hunting period (e.g., a period in which theapplication/client on the mobile device is seeking the longest timebetween a request and response that a given network will allow, a timingdiagram showing timing characteristics is illustrated in FIG. 8), asdetected by the long poll hunting detector 238 c of the applicationbehavior detector 236.

An example can be described below using T0, T1, T2, where T indicatesthe delay time between when a request is sent and when a response (e.g.,the response header) is detected/received for consecutive requests:

-   -   T0=Response0(t)−Request0(t)=180 s. (+/−tolerance)    -   T1=Response1(t)−Request1(t)=240 s. (+/−tolerance)    -   T2=Response2(t)−Request2(t)=500 s. (+/−tolerance)

In the example timing sequence shown above, T0<T1<T2, this may indicatea hunting pattern for a long poll when network timeout has not yet beenreached or exceeded. Furthermore, if the responses R0, R1, and R2received for the three requests are the same, R2 can be cached. In thisexample, R2 is cached during the long poll hunting period withoutwaiting for the long poll to settle, thus expediting response caching(e.g., this is optional accelerated caching behavior which can beimplemented for all or select applications).

As such, the local proxy 275 can specify information that can beextracted from the timing sequence shown above (e.g., polling schedule,polling interval, polling type) to the proxy server and begin cachingand to request the proxy server to begin polling and monitoring thesource (e.g., using any of T0, T1, T2 as polling intervals but typicallyT2, or the largest detected interval without timing out, and for whichresponses from the source is received will be sent to the proxy server325 of FIG. 3A for use in polling the content source (e.g., applicationserver/service provider 310)).

However, if the time intervals are detected to be getting shorter, theapplication (e.g., mobile application)/client may still be hunting for atime interval for which a response can be reliably received from thecontent source (e.g., application/server server/provider 110 or 310),and as such caching typically should not begin until therequest/response intervals indicate the same time interval or anincreasing time interval, for example, for a long poll type request.

An example of handling a detected decreasing delay can be describedbelow using T0, T1, T2, T3, and T4 where T indicates the delay timebetween when a request is sent and when a response (e.g., the responseheader) is detected/received for consecutive requests:

-   -   T0=Response0(t)−Request0(t)=160 s. (+/−tolerance)    -   T1=Response1(t)−Request1(t)=240 s. (+/−tolerance)    -   T2=Response2(t)−Request2(t)=500 s. (+/−tolerance)    -   T3=Time out at 700 s. (+/−tolerance)    -   T4=Response4(t)−Request4(t)=600 (+/−tolerance)

If a pattern for response delays T1<T2<T3>T4 is detected, as shown inthe above timing sequence (e.g., detected by the long poll huntingdetector 238 c of the application behavior detector 236), it can bedetermined that T3 likely exceeded the network time out during a longpoll hunting period. In Request 3, a response likely was not receivedsince the connection was terminated by the network, application, server,or other reason before a response was sent or available. On Request 4(after T4), if a response (e.g., Response 4) is detected or received,the local proxy 275 can then use the response for caching (if thecontent repeatability condition is met). The local proxy can also use T4as the poll interval in the polling schedule set for the proxy server tomonitor/poll the content source.

Note that the above description shows that caching can begin while longpolls are in hunting mode in the event of detecting increasing responsedelays, as long as responses are received and not timed out for a givenrequest. This can be referred to as the optional accelerated cachingduring long poll hunting. Caching can also begin after the hunting mode(e.g., after the poll requests have settled to a constant or nearconstant delay value) has completed. Note that hunting may or may notoccur for long polls and when hunting occurs; the proxy 275 cangenerally detect this and determine whether to begin to cache during thehunting period (increasing intervals with same responses) or wait untilthe hunt settles to a stable value.

In one embodiment, the timing predictor 246 a of the cacheappropriateness decision engine 246 can track timing of responsesreceived from outgoing requests from an application (e.g., mobileapplication) or client to detect any identifiable patterns which can bepartially wholly reproducible, such that locally cached responses can beprovided to the requesting client on the mobile device 250 in a mannerthat simulates content source (e.g., application server/content provider110 or 310) behavior. For example, the manner in which (e.g., from atiming standpoint) responses or content would be delivered to therequesting application/client on the device 250. This ensurespreservation of user experience when responses to application or mobileclient requests are served from a local and/or remote cache instead ofbeing retrieved/received directly from the content source (e.g.,application, content provider 110 or 310).

In one embodiment, the decision engine 246 or the timing predictor 246 adetermines the timing characteristics a given application (e.g., mobileapplication) or client from, for example, the request/response trackingengine 238 b and/or the application profile generator 239 (e.g., theresponse delay interval tracker 239 a). Using the timingcharacteristics, the timing predictor 246 a determines whether thecontent received in response to the requests are suitable or arepotentially suitable for caching. For example, poll request intervalsbetween two consecutive requests from a given application can be used todetermine whether request intervals are repeatable (e.g., constant, nearconstant, increasing with a pattern, decreasing with a pattern, etc.)and can be predicted and thus reproduced at least some of the timeseither exactly or approximated within a tolerance level.

In some instances, the timing characteristics of a given request typefor a specific application, for multiple requests of an application, orfor multiple applications can be stored in the application profilerepository 242. The application profile repository 242 can generallystore any type of information or metadata regarding applicationrequest/response characteristics including timing patterns, timingrepeatability, content repeatability, etc.

The application profile repository 242 can also store metadataindicating the type of request used by a given application (e.g., longpolls, long-held HTTP requests, HTTP streaming, push, COMET push, etc.)Application profiles indicating request type by applications can be usedwhen subsequent same/similar requests are detected, or when requests aredetected from an application which has already been categorized. In thismanner, timing characteristics for the given request type or forrequests of a specific application which has been tracked and/oranalyzed, need not be reanalyzed.

Application profiles can be associated with a time-to-live (e.g., or adefault expiration time). The use of an expiration time for applicationprofiles, or for various aspects of an application or request's profilecan be used on a case by case basis. The time-to-live or actualexpiration time of application profile entries can be set to a defaultvalue or determined individually, or a combination thereof. Applicationprofiles can also be specific to wireless networks, physical networks,network operators, or specific carriers.

One embodiment includes an application blacklist manager 201. Theapplication blacklist manager 201 can be coupled to the applicationcache policy repository 243 and can be partially or wholly internal tolocal proxy or the caching policy manager 245. Similarly, the blacklistmanager 201 can be partially or wholly internal to local proxy or theapplication behavior detector 236. The blacklist manager 201 canaggregate, track, update, manage, adjust, or dynamically monitor a listof destinations of servers/host that are ‘blacklisted,’ or identified asnot cached, on a permanent or temporary basis. The blacklist ofdestinations, when identified in a request, can potentially be used toallow the request to be sent over the (cellular) network for servicing.Additional processing on the request may not be performed since it isdetected to be directed to a blacklisted destination.

Blacklisted destinations can be identified in the application cachepolicy repository 243 by address identifiers including specific URIs orpatterns of identifiers including URI patterns. In general, blacklisteddestinations can be set by or modified for any reason by any partyincluding the user (owner/user of mobile device 250), operatingsystem/mobile platform of device 250, the destination itself, networkoperator (of cellular network), Internet service provider, other thirdparties, or according to a list of destinations for applications knownto be uncacheable/not suited for caching. Some entries in theblacklisted destinations may include destinations aggregated based onthe analysis or processing performed by the local proxy (e.g., cacheappropriateness decision engine 246).

For example, applications or mobile clients on the mobile device forwhich responses have been identified as non-suitable for caching can beadded to the blacklist. Their corresponding hosts/servers may be addedin addition to or in lieu of an identification of the requestingapplication/client on the mobile device 250. Some or all of such clientsidentified by the proxy system can be added to the blacklist. Forexample, for all application clients or applications that aretemporarily identified as not being suitable for caching, only thosewith certain detected characteristics (based on timing, periodicity,frequency of response content change, content predictability, size,etc.) can be blacklisted.

The blacklisted entries may include a list of requesting applications orrequesting clients on the mobile device (rather than destinations) suchthat, when a request is detected from a given application or givenclient, it may be sent through the network for a response, sinceresponses for blacklisted clients/applications are in most circumstancesnot cached.

A given application profile may also be treated or processed differently(e.g., different behavior of the local proxy 275 and the remote proxy325) depending on the mobile account associated with a mobile devicefrom which the application is being accessed. For example, a higherpaying account, or a premier account may allow more frequent access ofthe wireless network or higher bandwidth allowance thus affecting thecaching policies implemented between the local proxy 275 and proxyserver 325 with an emphasis on better performance compared toconservation of resources. A given application profile may also betreated or processed differently under different wireless networkconditions (e.g., based on congestion or network outage, etc.).

Note that cache appropriateness can be determined, tracked, and managedfor multiple clients or applications on the mobile device 250. Cacheappropriateness can also be determined for different requests or requesttypes initiated by a given client or application on the mobile device250. The caching policy manager 245, along with the timing predictor 246a and/or the content predictor 246 b which heuristically determines orestimates predictability or potential predictability, can track, manageand store cacheability information for various application or variousrequests for a given application. Cacheability information may alsoinclude conditions (e.g., an application can be cached at certain timesof the day, or certain days of the week, or certain requests of a givenapplication can be cached, or all requests with a given destinationaddress can be cached) under which caching is appropriate which can bedetermined and/or tracked by the cache appropriateness decision engine246 and stored and/or updated when appropriate in the application cachepolicy repository 243 coupled to the cache appropriateness decisionengine 246.

The information in the application cache policy repository 243 regardingcacheability of requests, applications, and/or associated conditions canbe used later on when same requests are detected. In this manner, thedecision engine 246 and/or the timing and content predictors 246 a/bneed not track and reanalyze request/response timing and contentcharacteristics to make an assessment regarding cacheability. Inaddition, the cacheability information can in some instances be sharedwith local proxies of other mobile devices by way of directcommunication or via the host server (e.g., proxy server 325 of hostserver 300).

For example, cacheability information detected by the local proxy 275 onvarious mobile devices can be sent to a remote host server or a proxyserver 325 on the host server (e.g., host server 300 or proxy server 325shown in the example of FIG. 3A, host 100 and proxy server 125 in theexample of FIG. 1A-B). The remote host or proxy server can thendistribute the information regarding application-specific,request-specific cacheability information and/or any associatedconditions to various mobile devices or their local proxies in awireless network or across multiple wireless networks (same serviceprovider or multiple wireless service providers) for their use.

In general, the selection criteria for caching can further include, byway of example but not limitation, the state of the mobile deviceindicating whether the mobile device is active or inactive, networkconditions, and/or radio coverage statistics. The cache appropriatenessdecision engine 246 can in any one or any combination of the criteria,and in any order, identifying sources for which caching may be suitable.

Once application servers/content providers having identified or detectedcontent that is potentially suitable for local caching on the mobiledevice 250, the cache policy manager 245 can proceed to cache theassociated content received from the identified sources by storingcontent received from the content source as cache elements in a localcache (e.g., local cache 185 or 285 shown in the examples of FIG. 1B andFIG. 2A, respectively) on the mobile device 250.

The response can be stored in the cache 285 (e.g., also referred as thelocal cache) as a cache entry. In addition to the response to a request,the cached entry can include response metadata having additionalinformation regarding caching of the response. The metadata may begenerated by the metadata generator 203 and can include, for example,timing data such as the access time of the cache entry or creation timeof the cache entry. Metadata can include additional information, such asany information suited for use in determining whether the responsestored as the cached entry is used to satisfy the subsequent response.For example, metadata information can further include, request timinghistory (e.g., including request time, request start time, request endtime), hash of the request and/or response, time intervals or changes intime intervals, etc.

The cache entry is typically stored in the cache 285 in association witha time-to-live (TTL), which for example may be assigned or determined bythe TTL manager 244 a of the cache invalidator 244. The time-to-live ofa cache entry is the amount of time the entry is persisted in the cache285 regardless of whether the response is still valid or relevant for agiven request or client/application on the mobile device 250. Forexample, if the time-to-live of a given cache entry is set to 12 hours,the cache entry is purged, removed, or otherwise indicated as havingexceeded the time-to-live, even if the response body contained in thecache entry is still current and applicable for the associated request.

A default time-to-live can be automatically used for all entries unlessotherwise specified (e.g., by the TTL manager 244 a), or each cacheentry can be created with its individual TTL (e.g., determined by theTTL manager 244 a based on various dynamic or static criteria). Notethat each entry can have a single time-to-live associated with both theresponse data and any associated metadata. In some instances, theassociated metadata may have a different time-to-live (e.g., a longertime-to-live) than the response data.

The content source having content for caching can, in addition or inalternate, be identified to a proxy server (e.g., proxy server 125 or325 shown in the examples of FIG. 1B and FIG. 3A, respectively) remotefrom and in wireless communication with the mobile device 250 such thatthe proxy server can monitor the content source (e.g., applicationserver/content provider 110) for new or changed data. Similarly, thelocal proxy (e.g., the local proxy 175 or 275 of FIG. 1B and FIG. 2A,respectively) can identify to the proxy server that content receivedfrom a specific application server/content provider is being stored ascached elements in the local cache 285.

Once content has been locally cached, the cache policy manager 245, uponreceiving future polling requests to contact the applicationserver/content host (e.g., 110 or 310), can retrieve the cached elementsfrom the local cache to respond to the polling request made at themobile device 250 such that a radio of the mobile device is notactivated to service the polling request. For example, the cache look-upengine 205 can query the cache 285 to identify the response to be servedto a response. The response can be served from the cache in response toidentifying a matching cache entry and also using any metadata storedwith the response in the cache entry. The cache entries can be queriedby the cache look-up engine using a URI of the request or another typeof identifier (e.g., via the ID or URI filter 205 a). The cache-lookupengine 205 can further use the metadata (e.g., extract any timinginformation or other relevant information) stored with the matchingcache entry to determine whether response is still suited for use inbeing served to a current request.

Note that the cache-look-up can be performed by the engine 205 using oneor more of various multiple strategies. In one embodiment, multiplecook-up strategies can be executed sequentially on each entry store dinthe cache 285, until at least one strategy identifies a matching cacheentry. The strategy employed to performing cache look-up can include astrict matching criteria or a matching criteria which allows fornon-matching parameters.

For example, the look-up engine 205 can perform a strict matchingstrategy which searches for an exact match between an identifier (e.g.,a URI for a host or resource) referenced in a present request for whichthe proxy is attempting to identify a cache entry and an identifierstored with the cache entries. In the case where identifiers includeURIs or URLs, the matching algorithm for strict matching will search fora cache entry where all the parameters in the URLs match. For example:

Example 1

-   -   1. Cache contains entry for http://test.com/products/    -   2. Request is being made to URI http://test.com/products/Strict        strategy will find a match, since both URIs are same.

Example 2

-   -   1. Cache contains entry for http://test.com/products/?query=all    -   2. Request is being made to URI        http://test.com/products/?query=sub

Under the strict strategy outlined above, a match will not be foundsince the URIs differ in the query parameter.

In another example strategy, the look-up engine 205 looks for a cacheentry with an identifier that partially matches the identifierreferences in a present request for which the proxy is attempting toidentify a matching cache entry. For example, the look-up engine 205 maylook for a cache entry with an identifier which differs from the requestidentifier by a query parameter value. In utilizing this strategy, thelook-up engine 205 can collect information collected for multipleprevious requests (e.g., a list of arbitrary parameters in anidentifier) to be later checked with the detected arbitrary parameter inthe current request. For example, in the case where cache entries arestored with URI or URL identifiers, the look-up engine searches for acache entry with a URI differing by a query parameter. If found, theengine 205 can examine the cache entry for information collected duringprevious requests (e.g. a list of arbitrary parameters) and checkedwhether the arbitrary parameter detected in or extracted from thecurrent URI/URL belongs to the arbitrary parameters list.

Example 1

-   -   1. Cache contains entry for http://test.com/products/?query=all,        where query is marked as arbitrary.    -   2. Request is being made to URI        http://text.com/products/?query=sub        Match will be found, since query parameter is marked as        arbitrary.

Example 2

-   -   1. Cache contains entry for http://test.com/products/?query=all,        where query is marked as arbitrary.    -   2. Request is being made to URI        http://test.com/products/?query=sub&sort=asc        Match will not be found, since current request contains sort        parameter which is not marked as arbitrary in the cache entry.

Additional strategies for detecting cache hit may be employed. Thesestrategies can be implemented singly or in any combination thereof. Acache-hit can be determined when any one of these strategies determinesa match. A cache miss may be indicated when the look-up engine 205determines that the requested data cannot be served from the cache 285,for any reason. For example, a cache miss may be determined when nocache entries are identified for any or all utilized look-up strategies.

Cache miss may also be determined when a matching cache entry exists butdetermined to be invalid or irrelevant for the current request. Forexample, the look-up engine 205 may further analyze metadata (e.g.,which may include timing data of the cache entry) associated with thematching cache entry to determine whether it is still suitable for usein responding to the present request.

When the look-up engine 205 has identified a cache hit (e.g., an eventindicating that the requested data can be served from the cache), thestored response in the matching cache entry can be served from the cacheto satisfy the request of an application/client.

By servicing requests using cache entries stored in cache 285, networkbandwidth and other resources need not be used to request/receive pollresponses which may have not changed from a response that has alreadybeen received at the mobile device 250. Such servicing and fulfillingapplication (e.g., mobile application) requests locally via cacheentries in the local cache 285 allows for more efficient resource andmobile network traffic utilization and management since the request neednot be sent over the wireless network further consuming bandwidth. Ingeneral, the cache 285 can be persisted between power on/off of themobile device 250, and persisted across application/client refreshes andrestarts.

For example, the local proxy 275, upon receipt of an outgoing requestfrom its mobile device 250 or from an application or other type ofclient on the mobile device 250, can intercept the request and determinewhether a cached response is available in the local cache 285 of themobile device 250. If so, the outgoing request is responded to by thelocal proxy 275 using the cached response on the cache of the mobiledevice. As such, the outgoing request can be filled or satisfied withouta need to send the outgoing request over the wireless network, thusconserving network resources and battery consumption.

In one embodiment, the responding to the requesting application/clienton the device 250 is timed to correspond to a manner in which thecontent server would have responded to the outgoing request over apersistent connection (e.g., over the persistent connection, orlong-held HTTP connection, long poll type connection, that would havebeen established absent interception by the local proxy). The timing ofthe response can be emulated or simulated by the local proxy 275 topreserve application behavior such that end user experience is notaffected, or minimally affected by serving stored content from the localcache 285 rather than fresh content received from the intended contentsource (e.g., content host/application server 110 of FIG. 1A-B). Thetiming can be replicated exactly or estimated within a toleranceparameter, which may go unnoticed by the user or treated similarly bythe application so as to not cause operation issues.

For example, the outgoing request can be a request for a persistentconnection intended for the content server (e.g., applicationserver/content provider of examples of FIG. 1A-1B). In a persistentconnection (e.g., long poll, COMET-style push or any other pushsimulation in asynchronous HTTP requests, long-held HTTP request, HTTPstreaming, or others) with a content source (server), the connection isheld for some time after a request is sent. The connection can typicallybe persisted between the mobile device and the server until content isavailable at the server to be sent to the mobile device. Thus, theretypically can be some delay in time between when a long poll request issent and when a response is received from the content source. If aresponse is not provided by the content source for a certain amount oftime, the connection may also terminate due to network reasons (e.g.,socket closure) if a response is not sent.

Thus, to emulate a response from a content server sent over a persistentconnection (e.g., a long poll style connection), the manner of responseof the content server can be simulated by allowing a time interval toelapse before responding to the outgoing request with the cachedresponse. The length of the time interval can be determined on a requestby request basis or on an application by application (client by clientbasis), for example.

In one embodiment, the time interval is determined based on requestcharacteristics (e.g., timing characteristics) of an application on themobile device from which the outgoing request originates. For example,poll request intervals (e.g., which can be tracked, detected, anddetermined by the long poll detector 238 a of the poll interval detector238) can be used to determine the time interval to wait beforeresponding to a request with a local cache entry and managed by theresponse scheduler 249 a.

One embodiment of the cache policy manager 245 includes a poll schedulegenerator 247 which can generate a polling schedule for one or moreapplications on the mobile device 250. The polling schedule can specifya polling interval that can be employed by an entity which is physicallydistinct and/or separate from the mobile device 250 in monitoring thecontent source for one or more applications (such that cached responsescan be verified periodically by polling a host server (host server 110or 310) to which the request is directed) on behalf of the mobiledevice. One example of such an external entity which can monitor thecontent at the source for the mobile device 250 is a proxy server (e.g.,proxy server 125 or 325 shown in the examples of FIG. 1B and FIG. 3A-C).

The polling schedule (e.g., including a rate/frequency of polling) canbe determined, for example, based on the interval between the pollingrequests directed to the content source from the mobile device. Thepolling schedule or rate of polling may be determined at the mobiledevice 250 (by the local proxy). In one embodiment, the poll intervaldetector 238 of the application behavior detector 236 can monitorpolling requests directed to a content source from the mobile device 250in order to determine an interval between the polling requests made fromany or all application (e.g., mobile application).

For example, the poll interval detector 238 can track requests andresponses for applications or clients on the device 250. In oneembodiment, consecutive requests are tracked prior to detection of anoutgoing request initiated from the application (e.g., mobileapplication) on the mobile device 250 by the same mobile client orapplication (e.g., mobile application). The polling rate can bedetermined using request information collected for the request for whichthe response is cached. In one embodiment, the rate is determined fromaverages of time intervals between previous requests generated by thesame client which generated the request. For example, a first intervalmay be computed between the current request and a previous request, anda second interval can be computed between the two previous requests. Thepolling rate can be set from the average of the first interval and thesecond interval and sent to the proxy server in setting up the cachingstrategy.

Alternate intervals may be computed in generating an average; forexample, multiple previous requests in addition to two previous requestsmay be used, and more than two intervals may be used in computing anaverage. In general, in computing intervals, a given request need nothave resulted in a response to be received from the host server/contentsource in order to use it for interval computation. In other words, thetiming characteristics of a given request may be used in intervalcomputation, as long as the request has been detected, even if therequest failed in sending, or if the response retrieval failed.

One embodiment of the poll schedule generator 247 includes a scheduleupdate engine 247 a and/or a time adjustment engine 247 b. The scheduleupdate engine 247 a can determine a need to update a rate or pollinginterval with which a given application server/content host from apreviously set value, based on a detected interval change in the actualrequests generated from a client or application (e.g., mobileapplication) on the mobile device 250.

For example, a request for which a monitoring rate was determined maynow be sent from the application (e.g., mobile application) or client ata different request interval. The scheduled update engine 247 a candetermine the updated polling interval of the actual requests andgenerate a new rate, different from the previously set rate to poll thehost at on behalf of the mobile device 250. The updated polling rate canbe communicated to the remote proxy (proxy server 325) over the cellularnetwork for the remote proxy to monitor the given host. In someinstances, the updated polling rate may be determined at the remoteproxy or remote entity which monitors the host.

In one embodiment, the time adjustment engine 247 b can further optimizethe poll schedule generated to monitor the application server/contentsource (110 or 310). For example, the time adjustment engine 247 b canoptionally specify a time to start polling to the proxy server. Forexample, in addition to setting the polling interval at which the proxyserver is to monitor the application, server/content host can alsospecify the time at which an actual request was generated at the mobileclient/application.

However, in some cases, due to inherent transmission delay or addednetwork delays or other types of latencies, the remote proxy serverreceives the poll setup from the local proxy with some delay (e.g., afew minutes, or a few seconds). This has the effect of detectingresponse change at the source after a request is generated by the mobileclient/application causing the invalidate of the cached response tooccur after it has once again been served to the application after theresponse is no longer current or valid. This discrepancy is furtherillustrated diagrammatically in the data timing diagram of FIG. 21.

To resolve this non-optimal result of serving the out-dated content onceagain before invalidating it, the time adjustment engine 247 b canspecify the time (t0) at which polling should begin in addition to therate, where the specified initial time t0 can be specified to the proxyserver 325 as a time that is less than the actual time when the requestwas generated by the mobile app/client. This way, the server polls theresource slightly before the generation of an actual request by themobile client such that any content change can be detected prior to anactual application request. This prevents invalid or irrelevantout-dated content/response from being served once again before freshcontent is served.

In one embodiment, an outgoing request from a mobile device 250 isdetected to be for a persistent connection (e.g., a long poll, COMETstyle push, and long-held (HTTP) request) based on timingcharacteristics of prior requests from the same application or client onthe mobile device 250. For example, requests and/or correspondingresponses can be tracked by the request/response tracking engine 238 bof the long poll detector 238 a of the poll interval detector 238.

The timing characteristics of the consecutive requests can be determinedto set up a polling schedule for the application or client. The pollingschedule can be used to monitor the content source (contentsource/application server) for content changes such that cached contentstored on the local cache in the mobile device 250 can be appropriatelymanaged (e.g., updated or discarded). In one embodiment, the timingcharacteristics can include, for example, a response delay time (‘D’)and/or an idle time (‘IT’).

In one embodiment, the response/request tracking engine 238 b can trackrequests and responses to determine, compute, and/or estimate, thetiming diagrams for applicant or client requests.

For example, the response/request tracking engine 238 b detects a firstrequest (Request 0) initiated by a client on the mobile device and asecond request (Request 1) initiated by the client on the mobile deviceafter a response is received at the mobile device responsive to thefirst request. The second request is one that is subsequent to the firstrequest.

In one embodiment, the response/request tracking engine 238 b can trackrequests and responses to determine, compute, and/or estimate the timingdiagrams for applicant or client requests. The response/request trackingengine 238 b can detect a first request initiated by a client on themobile device and a second request initiated by the client on the mobiledevice after a response is received at the mobile device responsive tothe first request. The second request is one that is subsequent to thefirst request.

The response/request tracking engine 238 b further determines relativetimings between the first, second requests, and the response received inresponse to the first request. In general, the relative timings can beused by the long poll detector 238 a to determine whether requestsgenerated by the application are long poll requests.

Note that in general, the first and second requests that are used by theresponse/request tracking engine 238 b in computing the relative timingsare selected for use after a long poll hunting period has settled or inthe event when long poll hunting does not occur.

In one embodiment, the long poll hunting detector 238 c can identify ordetect hunting mode, by identifying increasing request intervals (e.g.,increasing delays). The long poll hunting detector 238 a can also detecthunting mode by detecting increasing request intervals, followed by arequest with no response (e.g., connection timed out), or by detectingincreasing request intervals followed by a decrease in the interval. Inaddition, the long poll hunting detector 238 c can apply a filter valueor a threshold value to request-response time delay value (e.g., anabsolute value) above which the detected delay can be considered to be along poll request-response delay. The filter value can be any suitablevalue characteristic of long polls and/or network conditions (e.g., 2 s,5 s, 10 s, 15 s, 20 s., etc.) and can be used as a filter or thresholdvalue.

The response delay time (‘D’) refers to the start time to receive aresponse after a request has been sent and the idle refers to time tosend a subsequent request after the response has been received. In oneembodiment, the outgoing request is detected to be for a persistentconnection based on a comparison (e.g., performed by the tracking engine238 b) of the response delay time relative (‘D’) or average of (‘D’)(e.g., any average over any period of time) to the idle time (‘IT’), forexample, by the long poll detector 238 a. The number of averages usedcan be fixed, dynamically adjusted, or changed over a longer period oftime. For example, the requests initiated by the client are determinedto be long poll requests if the response delay time interval is greaterthan the idle time interval (D>IT or D>>IT). In one embodiment, thetracking engine 238 b of the long poll detector computes, determines, orestimates the response delay time interval as the amount of time elapsedbetween time of the first request and initial detection or full receiptof the response.

In one embodiment, a request is detected to be for a persistentconnection when the idle time (‘IT’) is short since persistentconnections, established in response to long poll requests or long pollHTTP requests for example, can also be characterized in detectingimmediate or near-immediate issuance of a subsequent request afterreceipt of a response to a previous request (e.g., IT ˜0). As such, theidle time (‘IT’) can also be used to detect such immediate ornear-immediate re-request to identify long poll requests. The absoluteor relative timings determined by the tracking engine 238 b are used todetermine whether the second request is immediately or near-immediatelyre-requested after the response to the first request is received. Forexample, a request may be categorized as a long poll request ifD+RT+IT˜D+RT since IT is small for this to hold true. IT may bedetermined to be small if it is less than a threshold value. Note thatthe threshold value could be fixed or calculated over a limited timeperiod (a session, a day, a month, etc.), or calculated over a longertime period (e.g., several months or the life of the analysis). Forexample, for every request, the average IT can be determined, and thethreshold can be determined using this average IT (e.g., the average ITless a certain percentage may be used as the threshold). This can allowthe threshold to automatically adapt over time to network conditions andchanges in server capability, resource availability or server response.A fixed threshold can take upon any value including by way of examplebut not limitation (e.g., 1 s. 2 s. 3 s. . . . etc.).

In one embodiment, the long poll detector 238 a can compare the relativetimings (e.g., determined by the tracker engine 238 b) torequest-response timing characteristics for other applications todetermine whether the requests of the application are long pollrequests. For example, the requests initiated by a client or applicationcan be determined to be long poll requests if the response delayinterval time (‘D’) or the average response delay interval time (e.g.,averaged over x number of requests or any number of delay interval timesaveraged over x amount of time) is greater than a threshold value.

The threshold value can be determined using response delay intervaltimes for requests generated by other clients, for example by therequest/response tracking engine 238 b and/or by the application profilegenerator 239 (e.g., the response delay interval tracker 239 a). Theother clients may reside on the same mobile device and the thresholdvalue is determined locally by components on the mobile device. Thethreshold value can be determined for all requests over all resourcesserver over all networks, for example. The threshold value can be set toa specific constant value (e.g., 30 seconds, for example) to be used forall requests, or any request which does not have an applicable thresholdvalue (e.g., long poll is detected if D>30 seconds).

In some instances, the other clients reside on different mobile devicesand the threshold can be determined by a proxy server (e.g., proxyserver 325 of the host 300 shown in the example of FIG. 3A-B) which isexternal to the mobile device and able to communicate over a wirelessnetwork with the multiple different mobile devices, as will be furtherdescribed with reference to FIG. 3B.

In one embodiment, the cache policy manager 245 sends the pollingschedule to the proxy server (e.g., proxy server 125 or 325 shown in theexamples of FIG. 1B and FIG. 3A) and can be used by the proxy server inmonitoring the content source, for example, for changed or new content(updated response different from the cached response associated with arequest or application). A polling schedule sent to the proxy caninclude multiple timing parameters including but not limited to interval(time from request 1 to request 2) or a time out interval (time to waitfor response, used in long polls, for example). The timing intervals‘RI’, ‘D’, ‘RT’, and/or ‘IT’, or some statistical manipulation of theabove values (e.g., average, standard deviation, etc.) may all or inpart be sent to the proxy server.

For example, in the case when the local proxy 275 detects a long poll,the various timing intervals in a request/response timing sequence(e.g., ‘D’, ‘RT’, and/or ‘IT’) can be sent to the proxy server 325 foruse in polling the content source (e.g., application server/content host110). The local proxy 275 can also identify to the proxy server 325 thata given application or request to be monitored is a long poll request(e.g., instructing the proxy server to set a ‘long poll flag’, forexample). In addition, the proxy server uses the various timingintervals to determine when to send keep-alive indications on behalf ofmobile devices.

The local cache invalidator 244 of the caching policy manager 245 caninvalidate cache elements in the local cache (e.g., cache 185 or 285)when new or changed data (e.g., updated response) is detected from theapplication server/content source for a given request. The cachedresponse can be determined to be invalid for the outgoing request basedon a notification received from the proxy server (e.g., proxy 325 or thehost server 300). The source which provides responses to requests of themobile client can be monitored to determine relevancy of the cachedresponse stored in the cache of the mobile device 250 for the request.For example, the cache invalidator 244 can further remove/delete thecached response from the cache of the mobile device when the cachedresponse is no longer valid for a given request or a given application.

In one embodiment, the cached response is removed from the cache afterit is provided once again to an application which generated the outgoingrequest after determining that the cached response is no longer valid.The cached response can be provided again without waiting for the timeinterval or provided again after waiting for a time interval (e.g., thetime interval determined to be specific to emulate the response delay ina long poll). In one embodiment, the time interval is the response delay‘D’ or an average value of the response delay ‘D’ over two or morevalues.

The new or changed data can be, for example, detected by the proxyserver (e.g., proxy server 125 or 325 shown in the examples of FIG. 1Band FIG. 3A). When a cache entry for a given request/poll has beeninvalidated, the use of the radio on the mobile device 250 can beenabled (e.g., by the local proxy 275 or the cache policy manager 245)to satisfy the subsequent polling requests, as further described withreference to the interaction diagram of FIG. 4B.

One embodiment of the cache policy manager 245 includes a cache orconnect selection engine 249 which can decide whether to use a locallycached entry to satisfy a poll/content request generated at the mobiledevice 250 by an application or widget. For example, the local proxy 275or the cache policy manger 245 can intercept a polling request, made byan application (e.g., mobile application) on the mobile device, tocontact the application server/content provider. The selection engine249 can determine whether the content received for the interceptedrequest has been locally stored as cache elements for deciding whetherthe radio of the mobile device needs to be activated to satisfy therequest made by the application (e.g., mobile application) and alsodetermine whether the cached response is still valid for the outgoingrequest prior to responding to the outgoing request using the cachedresponse.

In one embodiment, the local proxy 275, in response to determining thatrelevant cached content exists and is still valid, can retrieve thecached elements from the local cache to provide a response to theapplication (e.g., mobile application) which made the polling requestsuch that a radio of the mobile device is not activated to provide theresponse to the application (e.g., mobile application). In general, thelocal proxy 275 continues to provide the cached response each time theoutgoing request is received until the updated response different fromthe cached response is detected.

When it is determined that the cached response is no longer valid, a newrequest for a given request is transmitted over the wireless network foran updated response. The request can be transmitted to the applicationserver/content provider (e.g., server/host 110) or the proxy server onthe host server (e.g., proxy 325 on the host 300) for a new and updatedresponse. In one embodiment the cached response can be provided again asa response to the outgoing request if a new response is not receivedwithin the time interval, prior to removal of the cached response fromthe cache on the mobile device.

FIG. 2C depicts a block diagram illustrating another example ofcomponents in the application behavior detector 236 and the cachingpolicy manager 245 in the local proxy 275 on the client-side of thedistributed proxy system shown in the example of FIG. 2A. Theillustrated application behavior detector 236 and the caching policymanager 245 can, for example, enable the local proxy 275 to detect cachedefeat and perform caching of content addressed by identifiers intendedto defeat cache.

In one embodiment, the caching policy manager 245 includes a cachedefeat resolution engine 221, an identifier formalizer 211, a cacheappropriateness decision engine 246, a poll schedule generator 247, anapplication protocol module 248, a cache or connect selection engine 249having a cache query module 229, and/or a local cache invalidator 244.The cache defeat resolution engine 221 can further include a patternextraction module 222 and/or a cache defeat parameter detector 223. Thecache defeat parameter detector 223 can further include a randomparameter detector 224 and/or a time/date parameter detector 226. Oneembodiment further includes an application cache policy repository 243coupled to the decision engine 246.

In one embodiment, the application behavior detector 236 includes apattern detector 237, a poll interval detector 238, an applicationprofile generator 239, and/or a priority engine 241. The patterndetector 237 can further include a cache defeat parameter detector 223having also, for example, a random parameter detector 233 and/or atime/date parameter detector 234. One embodiment further includes anapplication profile repository 242 coupled to the application profilegenerator 239. The application profile generator 239, and the priorityengine 241 have been described in association with the description ofthe application behavior detector 236 in the example of FIG. 2A.

The cache defeat resolution engine 221 can detect, identify, track,manage, and/or monitor content or content sources (e.g., servers orhosts) which employ identifiers and/or are addressed by identifiers(e.g., resource identifiers such as URLs and/or URIs) with one or moremechanisms that defeat cache or are intended to defeat cache. The cachedefeat resolution engine 221 can, for example, detect from a given datarequest generated by an application or client that the identifierdefeats or potentially defeats cache, where the data request otherwiseaddresses content or responses from a host or server (e.g., applicationserver/content host 110 or 310) that is cacheable.

In one embodiment, the cache defeat resolution engine 221 detects oridentifies cache defeat mechanisms used by content sources (e.g.,application server/content host 110 or 310) using the identifier of adata request detected at the mobile device 250. The cache defeatresolution engine 221 can detect or identify a parameter in theidentifier which can indicate that cache defeat mechanism is used. Forexample, a format, syntax, or pattern of the parameter can be used toidentify cache defeat (e.g., a pattern, format, or syntax as determinedor extracted by the pattern extraction module 222).

The pattern extraction module 222 can parse an identifier into multipleparameters or components and perform a matching algorithm on eachparameter to identify any of which match one or more predeterminedformats (e.g., a date and/or time format, as illustrated in parameters702 shown in FIG. 7). For example, the results of the matching or theparsed out parameters from an identifier can be used (e.g., by the cachedefeat parameter detector 223) to identify cache defeating parameterswhich can include one or more changing parameters.

The cache defeat parameter detector 223, in one embodiment can detectrandom parameters (e.g., by the random parameter detector 224) and/ortime and/or date parameters which are typically used for cache defeat.The cache defeat parameter detector 223 can detect random parameters(e.g., as illustrated in parameters 752 shown in FIG. 7) and/ortime/dates using commonly employed formats for these parameters andperforming pattern matching algorithms and tests.

In addition to detecting patterns, formats, and/or syntaxes, the cachedefeat parameter detector 223 further determines or confirms whether agiven parameter is defeating cache and whether the addressed content canbe cached by the distributed caching system. The cache defeat parameterdetector 223 can detect this by analyzing responses received for theidentifiers utilized by a given data request. In general, a changingparameter in the identifier is identified to indicate cache defeat whenresponses corresponding to multiple data requests are the same even whenthe multiple data requests uses identifiers with the changing parameterbeing different for each of the multiple data requests.

For example, at least two same responses may be required to identify thechanging parameter as indicating cache defeat. In some instances, atleast three same responses may be required. The requirement for thenumber of same responses needed to determine that a given parameter witha varying value between requests is cache defeating may be applicationspecific, context dependent, and/or user dependent/user specified, or acombination of the above. Such a requirement may also be static ordynamically adjusted by the distributed cache system to meet certainperformance thresholds and/or either explicit/implicit feedbackregarding user experience (e.g., whether the user or application isreceiving relevant/fresh content responsive to requests). More of thesame responses may be required to confirm cache defeat, or for thesystem to treat a given parameter as intended for cache defeat if anapplication begins to malfunction due to response caching and/or if theuser expresses dissatisfaction (explicit user feedback) or the systemdetects user frustration (implicit user cues).

The cache appropriateness decision engine 246 can detect, assess, ordetermine whether content from a content source (e.g., applicationserver/content provider 110 in the example of FIG. 1B) with which amobile device 250 interacts, has content that may be suitable forcaching. In some instances, content from a given applicationserver/content provider (e.g., the server/provider 110 of FIG. 1B) isdetermined to be suitable for caching based on a set of criteria (forexample, criteria specifying time criticality of the content that isbeing requested from the content source). In one embodiment, the localproxy (e.g., the local proxy 175 or 275 of FIG. 1B and FIG. 2A) appliesa selection criteria to store the content from the host server which isrequested by an application as cached elements in a local cache on themobile device to satisfy subsequent requests made by the application.

The selection criteria can also include, by way of example, but notlimitation, state of the mobile device indicating whether the mobiledevice is active or inactive, network conditions, and/or radio coveragestatistics. The cache appropriateness decision engine 246 can any one orany combination of the criteria, and in any order, in identifyingsources for which caching may be suitable.

Once application servers/content providers having identified or detectedcontent that is potentially suitable for local caching on the mobiledevice 250, the cache policy manager 245 can proceed to cache theassociated content received from the identified sources by storingcontent received from the content source as cache elements in a localcache (e.g., local cache 185 or 285 shown in the examples of FIG. 1B andFIG. 2A, respectively) on the mobile device 250. The content source canalso be identified to a proxy server (e.g., proxy server 125 or 325shown in the examples of FIG. 1B and FIG. 3A, respectively) remote fromand in wireless communication with the mobile device 250 such that theproxy server can monitor the content source (e.g., applicationserver/content provider 110) for new or changed data. Similarly, thelocal proxy (e.g., the local proxy 175 or 275 of FIG. 1B and FIG. 2A,respectively) can identify to the proxy server that content receivedfrom a specific application server/content provider is being stored ascached elements in the local cache.

In one embodiment, cache elements are stored in the local cache 285 asbeing associated with a normalized version of an identifier for anidentifier employing one or more parameters intended to defeat cache.The identifier can be normalized by the identifier normalizer module 211and the normalization process can include, by way of example, one ormore of: converting the URI scheme and host to lower-case, capitalizingletters in percent-encoded escape sequences, removing a default port,and removing duplicate slashes.

In another embodiment, the identifier is normalized by removing theparameter for cache defeat and/or replacing the parameter with a staticvalue which can be used to address or be associated with the cachedresponse received responsive to a request utilizing the identifier bythe normalizer 211 or the cache defeat parameter handler 212. Forexample, the cached elements stored in the local cache 285 (shown inFIG. 2A) can be identified using the normalized version of theidentifier or a hash value of the normalized version of the identifier.The hash value of an identifier or of the normalized identifier may begenerated by the hash engine 213.

Once content has been locally cached, the cache policy manager 245 can,upon receiving future polling requests to contact the content server,retrieve the cached elements from the local cache to respond to thepolling request made at the mobile device 250 such that a radio of themobile device is not activated to service the polling request. Suchservicing and fulfilling application (e.g., mobile application) requestslocally via local cache entries allow for more efficient resource andmobile network traffic utilization and management since networkbandwidth and other resources need not be used to request/receive pollresponses which may have not changed from a response that has alreadybeen received at the mobile device 250.

One embodiment of the cache policy manager 245 includes a poll schedulegenerator 247 which can generate a polling schedule for one or moreapplications on the mobile device 250. The polling schedule can specifya polling interval that can be employed by the proxy server (e.g., proxyserver 125 or 325 shown in the examples of FIG. 1B and FIG. 3A) inmonitoring the content source for one or more applications. The pollingschedule can be determined, for example, based on the interval betweenthe polling requests directed to the content source from the mobiledevice. In one embodiment, the poll interval detector 238 of theapplication behavior detector can monitor polling requests directed to acontent source from the mobile device 250 in order to determine aninterval between the polling requests made from any or all application(e.g., mobile application).

In one embodiment, the cache policy manager 245 sends the pollingschedule is sent to the proxy server (e.g., proxy server 125 or 325shown in the examples of FIG. 1B and FIG. 3A) and can be used by theproxy server in monitoring the content source, for example, for changedor new content. The local cache invalidator 244 of the caching policymanager 245 can invalidate cache elements in the local cache (e.g.,cache 185 or 285) when new or changed data is detected from theapplication server/content source for a given request. The new orchanged data can be, for example, detected by the proxy server. When acache entry for a given request/poll has been invalidated and/or removed(e.g., deleted from cache) after invalidation, the use of the radio onthe mobile device 250 can be enabled (e.g., by the local proxy or thecache policy manager 245) to satisfy the subsequent polling requests, asfurther described with reference to the interaction diagram of FIG. 4B.

In another embodiment, the proxy server (e.g., proxy server 125 or 325shown in the examples of FIG. 1B and FIG. 3A) uses a modified version ofa resource identifier used in a data request to monitor a given contentsource (the application server/content host 110 of FIG. 1A and FIG. 1Bto which the data request is addressed) for new or changed data. Forexample, in the instance where the content source or identifier isdetected to employ cache defeat mechanisms, a modified (e.g.,normalized) identifier can be used instead to poll the content source.The modified or normalized version of the identifier can be communicatedto the proxy server by the caching policy manager 245, or morespecifically the cache defeat parameter handler 212 of the identifiernormalizer 211.

The modified identifier used by the proxy server to poll the contentsource on behalf of the mobile device/application (e.g., mobileapplication) may or may not be the same as the normalized identifier.For example, the normalized identifier may be the original identifierwith the changing cache defeating parameter removed whereas the modifiedidentifier uses a substitute parameter in place of the parameter that isused to defeat cache (e.g., the changing parameter replaced with astatic value or other predetermined value known to the local proxyand/or proxy server). The modified parameter can be determined by thelocal proxy 275 and communicated to the proxy server. The modifiedparameter may also be generated by the proxy server (e.g., by theidentifier modifier module 353 shown in the example of FIG. 3C).

One embodiment of the cache policy manager 245 includes a cache orconnect selection engine 249 which can decide whether to use a locallycached entry to satisfy a poll/content request generated at the mobiledevice 250 by an application or widget. For example, the local proxy 275or the cache policy manger 245 can intercept a polling request made byan application (e.g., mobile application) on the mobile device, tocontact the application server/content provider. The selection engine249 can determine whether the content received for the interceptedrequest has been locally stored as cache elements for deciding whetherthe a radio of the mobile device needs to be activated to satisfy therequest made by the application (e.g., mobile application). In oneembodiment, the local proxy 275, in response to determining thatrelevant cached content exists and is still valid, can retrieve thecached elements from the local cache to provide a response to theapplication (e.g., mobile application) which made the polling requestsuch that a radio of the mobile device is not activated to provide theresponse to the application (e.g., mobile application).

In one embodiment, the cached elements stored in the local cache 285(shown in FIG. 2A) can be identified using a normalized version of theidentifier or a hash value of the normalized version of the identifier,for example, using the cache query module 229. Cached elements can bestored with normalized identifiers which have cache defeating parametersremoved or otherwise replaced such that the relevant cached elements canbe identified and retrieved in the future to satisfy other requestsemploying the same type of cache defeat. For example, when an identifierutilized in a subsequent request is determined to be utilizing the samecache defeating parameter, the normalized version of this identifier canbe generated and used to identify a cached response stored in the mobiledevice cache to satisfy the data request. The hash value of anidentifier or of the normalized identifier may be generated by the hashengine 213 of the identifier normalizer 211.

FIG. 2D depicts a block diagram illustrating examples of additionalcomponents in the local proxy 275 shown in the example of FIG. 2A whichis further capable of performing mobile traffic categorization andpolicy implementation based on application behavior and/or useractivity.

In this embodiment of the local proxy 275, the user activity module 215further includes one or more of, a user activity tracker 215 a, a useractivity prediction engine 215 b, and/or a user expectation manager 215c. The application behavior detect 236 can further include aprioritization engine 241 a, a time criticality detection engine 241 b,an application state categorizer 241 c, and/or an application trafficcategorizer 241 d. The local proxy 275 can further include a backlightdetector 219 and/or a network configuration selection engine 251. Thenetwork configuration selection engine 251 can further include, one ormore of, a wireless generation standard selector 251 a, a data ratespecifier 251 b, an access channel selection engine 251 c, and/or anaccess point selector.

In one embodiment, the application behavior detector 236 is able todetect, determined, identify, or infer, the activity state of anapplication on the mobile device 250 to which traffic has originatedfrom or is directed to, for example, via the application statecategorizer 241 c and/or the traffic categorizer 241 d. The activitystate can be determined by whether the application is in a foreground orbackground state on the mobile device (via the application statecategorizer 241 c) since the traffic for a foreground application vs. abackground application may be handled differently.

In one embodiment, the activity state can be determined, detected,identified, or inferred with a level of certainty of heuristics, basedon the backlight status of the mobile device 250 (e.g., by the backlightdetector 219) or other software agents or hardware sensors on the mobiledevice, including but not limited to, resistive sensors, capacitivesensors, ambient light sensors, motion sensors, touch sensors, etc. Ingeneral, if the backlight is on, the traffic can be treated as being ordetermined to be generated from an application that is active or in theforeground, or the traffic is interactive. In addition, if the backlightis on, the traffic can be treated as being or determined to be trafficfrom user interaction or user activity, or traffic containing data thatthe user is expecting within some time frame.

In one embodiment, the activity state is determined based on whether thetraffic is interactive traffic or maintenance traffic. Interactivetraffic can include transactions from responses and requests generateddirectly from user activity/interaction with an application and caninclude content or data that a user is waiting or expecting to receive.Maintenance traffic may be used to support the functionality of anapplication which is not directly detected by a user. Maintenancetraffic can also include actions or transactions that may take place inresponse to a user action, but the user is not actively waiting for orexpecting a response.

For example, a mail or message delete action at a mobile device 250generates a request to delete the corresponding mail or message at theserver, but the user typically is not waiting for a response. Thus, sucha request may be categorized as maintenance traffic, or traffic having alower priority (e.g., by the prioritization engine 241 a) and/or is nottime-critical (e.g., by the time criticality detection engine 214 b).

Contrastingly, a mail ‘read’ or message ‘read’ request initiated by auser a the mobile device 250, can be categorized as ‘interactivetraffic’ since the user generally is waiting to access content or datawhen they request to read a message or mail. Similarly, such a requestcan be categorized as having higher priority (e.g., by theprioritization engine 241 a) and/or as being time critical/timesensitive (e.g., by the time criticality detection engine 241 b).

The time criticality detection engine 241 b can generally determine,identify, infer the time sensitivity of data contained in traffic sentfrom the mobile device 250 or to the mobile device from a host server(e.g., host 300) or application server (e.g., app server/content source110). For example, time sensitive data can include, status updates,stock information updates, IM presence information, email messages orother messages, actions generated from mobile gaming applications,webpage requests, location updates, etc. Data that is not time sensitiveor time critical, by nature of the content or request, can includerequests to delete messages, mark-as-read or edited actions,application-specific actions such as a add-friend or delete-friendrequest, certain types of messages, or other information which does notfrequently changing by nature, etc. In some instances when the data isnot time critical, the timing with which to allow the traffic to passthrough is set based on when additional data needs to be sent from themobile device 250. For example, traffic shaping engine 255 can align thetraffic with one or more subsequent transactions to be sent together ina single power-on event of the mobile device radio (e.g, using thealignment module 256 and/or the batching module 257). The alignmentmodule 256 can also align polling requests occurring close in timedirected to the same host server, since these request are likely to beresponded to with the same data.

In the alternate or in combination, the activity state can be determinedfrom assessing, determining, evaluating, inferring, identifying useractivity at the mobile device 250 (e.g., via the user activity module215). For example, user activity can be directly detected and trackedusing the user activity tracker 215 a. The traffic resulting therefromcan then be categorized appropriately for subsequent processing todetermine the policy for handling. Furthermore, user activity can bepredicted or anticipated by the user activity prediction engine 215 b.By predicting user activity or anticipating user activity, the trafficthus occurring after the prediction can be treated as resulting fromuser activity and categorized appropriately to determine thetransmission policy.

In addition, the user activity module 215 can also manage userexpectations (e.g., via the user expectation manager 215 c and/or inconjunction with the activity tracker 215 and/or the prediction engine215 b) to ensure that traffic is categorized appropriately such thatuser expectations are generally met. For example, a user-initiatedaction should be analyzed (e.g., by the expectation manager 215) todetermine or infer whether the user would be waiting for a response. Ifso, such traffic should be handled under a policy such that the userdoes not experience an unpleasant delay in receiving such a response oraction.

In one embodiment, an advanced generation wireless standard network isselected for use in sending traffic between a mobile device and a hostserver in the wireless network based on the activity state of theapplication on the mobile device for which traffic is originated from ordirected to. An advanced technology standards such as the 3G, 3.5G, 3G+,4G, or LTE network can be selected for handling traffic generated as aresult of user interaction, user activity, or traffic containing datathat the user is expecting or waiting for. Advanced generation wirelessstandard network can also be selected for to transmit data contained intraffic directed to the mobile device which responds to foregroundactivities.

In categorizing traffic and defining a transmission policy for mobiletraffic, a network configuration can be selected for use (e.g., by thenetwork configuration selection engine 251) on the mobile device 250 insending traffic between the mobile device and a proxy server (325)and/or an application server (e.g., app server/host 110). The networkconfiguration that is selected can be determined based on informationgathered by the application behavior module 236 regarding applicationactivity state (e.g., background or foreground traffic), applicationtraffic category (e.g., interactive or maintenance traffic), anypriorities of the data/content, time sensitivity/criticality.

The network configuration selection engine 251 can select or specify oneor more of, a generation standard (e.g., via wireless generationstandard selector 251 a), a data rate (e.g., via data rate specifier 251b), an access channel (e.g., access channel selection engine 251 c),and/or an access point (e.g., vai the access point selector 251 d), inany combination.

For example, a more advanced generation (e.g, 3G, LTE, or 4G or later)can be selected or specified for traffic when the activity state is ininteraction with a user or in a foreground on the mobile device.Contrastingly, an older generation standard (e.g., 2G, 2.5G, or 3G orolder) can be specified for traffic when one or more of the following isdetected, the application is not interacting with the user, theapplication is running in the background on the mobile device, or thedata contained in the traffic is not time critical, or is otherwisedetermined to have lower priority.

Similarly, a network configuration with a slower data rate can bespecified for traffic when one or more of the following is detected, theapplication is not interacting with the user, the application is runningin the background on the mobile device, or the data contained in thetraffic is not time critical. The access channel (e.g., Forward accesschannel or dedicated channel) can be specified.

FIG. 2E depicts a block diagram illustrating examples of additionalcomponents in the traffic shaping engine 255 and the applicationbehavior detector 236 shown in the example of FIG. 2A which are furthercapable of facilitating alignment of incoming data transfer to a mobileor broadband device, or its user, to optimize the number of connectionsthat need to be established for receiving data over the wireless networkor broadband network.

In one embodiment of the local proxy 275, the traffic shaping engine255, in addition to the alignment module 256, batching module 257,further includes a poll interval adjuster 258. The poll intervaladjuster 258 can include a factor or denominator detection engine 258 a,a critical application detector 258 b, a critical interval identifier258 c, and/or a polling interval setting engine 258 d. Further in oneembodiment, the application behavior detector 236 of the local proxy 275further includes a poll interval detector 238.

In facilitating alignment of data bursts across various services orhosts to the mobile device 250, the local proxy 275 can initiallydetermine, detect, identify, compute, infer, extract the an original ordefault polling interval for applications or mobile clients on themobile device 250 (e.g., by the poll interval detector 238). Theoriginal or default polling interval is typically that characteristic ofthe mobile application itself and/or its host (e.g., its correspondingapplication server/content host 110 shown in FIG. 1A-1B). The pollinterval detector 238 can detect the original or default poll intervalfor any number or all of the mobile applications which regularly polltheir application servers or hosts for use by the proxy 275 ingenerating or adjusting the polling intervals suitable for use for thedevice 250 based on the applications installed thereon and theirrespective poll timing characteristics.

For example, the poll intervals (original or default) of the mobileclients or applications on device 250 can be used by the poll intervaladjuster 258. In general, an adjusted polling interval for a firstservice is generated based on a polling interval of a second service,which may be serviced by a distinct host from the first service (e.g.,Twitter=service 1; ESPN.com=service 2). The adjusted polling intervalcomputed for the first service and/or the second service, can be used inaligning at least some traffic received from the distinct hosts due toaccess on a mobile device of first and second services.

For example, in one embodiment, the adjusted polling interval of thefirst service can be a factor or denominator that the original pollinginterval of the first service has in common with the original pollinginterval of the second service (e.g., as determined by the factor ordenominator detection engine 258 a), and can further be determined basedon an original polling interval of the first service. Note that theadjusted polling interval of the first service need not be differentfrom the original polling interval of the first service when theoriginal polling interval of the first service and the polling intervalof the second service are factors or denominators of each other.

In one embodiment, the detection engine 258 a is able to furtherdetermine multiples of a factor or denominator of the polling intervalof the second service and the adjusted polling interval of the firstservice is a multiple of a factor or a multiple of a denominator of thepolling interval of the second service. In addition, the engine 258 acan determine multiples of a common factor or a common denominator of amajority number of the default polling intervals for multipleapplications on the device 250.

In addition, the adjusted polling interval of the first service can befurther determined, adjusted, or reconfigured (e.g., by the pollinginterval setting engine 258 d), based on time criticality of trafficfrom the first service relative to time criticality of traffic from thesecond service, or additional services on the mobile device 250. Forexample, the critical application detector 258 b can identify, detect,or receive input identifying or specifying one or more applications onthe device 250 as being more critical than others (e.g., higherpriority, time sensitive content/traffic, user preferred application, OSsponsored application, operator-sponsored content, etc.) and furtheradjust the polling intervals of the first and/or second services ifneed.

For example, the critical application detector 258 b can identify acritical application as being the most time critical of all applicationson the mobile device, or a set of applications for which data burstalignment is being applied or attempted on. For the critical application(s), the polling interval of the critical application is identified as aminimum critical interval (e.g., by the critical interval identifier 258c), which is not to be exceeded in assigning an updated polling intervalfor the critical application such that the priority in data needs (e.g.,whether it is a user-need, device-need, or application-need) for promptand timely delivery of data from the application server or content host.

High priority information/data or application can include, for example,financial data, sporting data or other data constantly changing innature, any data whose previous values have little to no relevancy, anydata (e.g., subscriptions or feeds) that a user wishes to be immediatelynotified of in real time or near real time, any specific featureindicated as a real time or near real time feature by the applicationserver/content host (e.g., real-time status updates, or real-timenotifications, high priority email or other messages, IM messages, etc.)or applications servicing any type of high priority/time sensitivecontent.

Once the polling intervals of one or more applications on the mobiledevice 250 have been set, the local proxy 275 communicates a pollingschedule including the adjusted polling interval (s) to a proxy server(eg., remote proxy 325 of FIG. 3A-3E) for use in aligning, in time, atleast some traffic received from the distinct hosts due to access on themobile device of first and second services, and any additional services.

In one embodiment, the poll interval setting engine 258 d also selectinga common starting point in time for an initial poll of the content hostsservicing the multiple applications. The poll interval setting engine258 d can set the start time to be anchored to the same absolute pointin time across the multiple applications on the device 250. In general,the application servers/content hosts are typically in UTC and use NTPto stay at the same time. For example, the interval setting engine 258 dcan pick any minute mark, second mark, hour mark, or other timeindicators, and communicate this to the remote proxy server (e.g., proxy325) as part of the adjusted polling parameter. The mark can be selectedrandomly used by all applications as the common ‘initial time t0.”

Note that while the above description uses an example of twoapplications, the same process can be performed for any number ofapplications or all applications/clients on the mobile device 250. Insome instances, some or all of the functions performed by one or more ofthe components poll interval adjustor 258 can be performed remotely, forexample, at a remote proxy server (e.g., proxy 325) using the pollintervals detected locally at the mobile device 250 (e.g., by the pollinterval detector 238). Note that the remote proxy (e.g., proxy 325) canreceive poll intervals for applications across multiple devices andtrack adjusted intervals for applications on multiple devices, as willbe further described with the example of FIG. 3E.

FIG. 3A depicts a block diagram illustrating an example of server-sidecomponents in a distributed proxy and cache system residing on a hostserver 300 that manages traffic in a wireless network for resourceconservation. The server-side proxy (or proxy server 325) can furthercategorize mobile traffic and/or implement delivery policies based onapplication behavior, content priority, user activity, and/or userexpectations.

The host server 300 generally includes, for example, a network interface308 and/or one or more repositories 312, 314, and 316. Note that server300 may be any portable/mobile or non-portable device, server, clusterof computers and/or other types of processing units (e.g., any number ofa machine shown in the example of FIG. 11) able to receive or transmitsignals to satisfy data requests over a network including any wired orwireless networks (e.g., WiFi, cellular, Bluetooth, etc.).

The network interface 308 can include networking module(s) or devices(s)that enable the server 300 to mediate data in a network with an entitythat is external to the host server 300, through any known and/orconvenient communications protocol supported by the host and theexternal entity. Specifically, the network interface 308 allows theserver 300 to communicate with multiple devices including mobile phonedevices 350 and/or one or more application servers/content providers310.

The host server 300 can store information about connections (e.g.,network characteristics, conditions, types of connections, etc.) withdevices in the connection metadata repository 312. Additionally, anyinformation about third party application or content providers can alsobe stored in the repository 312. The host server 300 can storeinformation about devices (e.g., hardware capability, properties, devicesettings, device language, network capability, manufacturer, devicemodel, OS, OS version, etc.) in the device information repository 314.Additionally, the host server 300 can store information about networkproviders and the various network service areas in the network serviceprovider repository 316.

The communication enabled by network interface 308 allows forsimultaneous connections (e.g., including cellular connections) withdevices 350 and/or connections (e.g., including wired/wireless, HTTP,Internet connections, LAN, WiFi, etc.) with content servers/providers310 to manage the traffic between devices 350 and content providers 310,for optimizing network resource utilization and/or to conserver power(battery) consumption on the serviced devices 350. The host server 300can communicate with mobile devices 350 serviced by different networkservice providers and/or in the same/different network service areas.The host server 300 can operate and is compatible with devices 350 withvarying types or levels of mobile capabilities, including by way ofexample but not limitation, 1G, 2G, 2G transitional (2.5G, 2.75G), 3G(IMT-2000), 3G transitional (3.5G, 3.75G, 3.9G), 4G (IMT-advanced), etc.

In general, the network interface 308 can include one or more of anetwork adaptor card, a wireless network interface card (e.g., SMSinterface, WiFi interface, interfaces for various generations of mobilecommunication standards including but not limited to 1G, 2G, 3G, 3.5G,4G type networks such as LTE, WiMAX, etc.), Bluetooth, WiFi, or anyother network whether or not connected via a router, an access point, awireless router, a switch, a multilayer switch, a protocol converter, agateway, a bridge, a bridge router, a hub, a digital media receiver,and/or a repeater.

The host server 300 can further include server-side components of thedistributed proxy and cache system which can include a proxy server 325and a server cache 335. In one embodiment, the proxy server 325 caninclude an HTTP access engine 345, a caching policy manager 355, a proxycontroller 365, a traffic shaping engine 375, a new data detector 347and/or a connection manager 395.

The HTTP access engine 345 may further include a heartbeat manager 398;the proxy controller 365 may further include a data invalidator module368; the traffic shaping engine 375 may further include a controlprotocol 376 and a batching module 377. Additional or lesscomponents/modules/engines can be included in the proxy server 325 andeach illustrated component.

As used herein, a “module,” a “manager,” a “handler,” a “detector,” an“interface,” a “controller,” a “normalizer,” a “generator,” an“invalidator,” or an “engine” includes a general purpose, dedicated orshared processor and, typically, firmware or software modules that areexecuted by the processor. Depending upon implementation-specific orother considerations, the module, manager, handler, detector, interface,controller, normalizer, generator, invalidator, or engine can becentralized or its functionality distributed. The module, manager,handler, detector, interface, controller, normalizer, generator,invalidator, or engine can include general or special purpose hardware,firmware, or software embodied in a computer-readable (storage) mediumfor execution by the processor. As used herein, a computer-readablemedium or computer-readable storage medium is intended to include allmediums that are statutory (e.g., in the United States, under 35 U.S.C.101), and to specifically exclude all mediums that are non-statutory innature to the extent that the exclusion is necessary for a claim thatincludes the computer-readable (storage) medium to be valid. Knownstatutory computer-readable mediums include hardware (e.g., registers,random access memory (RAM), non-volatile (NV) storage, to name a few),but may or may not be limited to hardware.

In the example of a device (e.g., mobile device 350) making anapplication or content request to an application server or contentprovider 310, the request may be intercepted and routed to the proxyserver 325 which is coupled to the device 350 and the applicationserver/content provider 310. Specifically, the proxy server is able tocommunicate with the local proxy (e.g., proxy 175 and 275 of theexamples of FIG. 1 and FIG. 2 respectively) of the mobile device 350,the local proxy forwards the data request to the proxy server 325 insome instances for further processing and, if needed, for transmissionto the application server/content server 310 for a response to the datarequest.

In such a configuration, the host 300, or the proxy server 325 in thehost server 300 can utilize intelligent information provided by thelocal proxy in adjusting its communication with the device in such amanner that optimizes use of network and device resources. For example,the proxy server 325 can identify characteristics of user activity onthe device 350 to modify its communication frequency. Thecharacteristics of user activity can be determined by, for example, theactivity/behavior awareness module 366 in the proxy controller 365 viainformation collected by the local proxy on the device 350.

In one embodiment, communication frequency can be controlled by theconnection manager 395 of the proxy server 325, for example, to adjustpush frequency of content or updates to the device 350. For instance,push frequency can be decreased by the connection manager 395 whencharacteristics of the user activity indicate that the user is inactive.In one embodiment, when the characteristics of the user activityindicate that the user is subsequently active after a period ofinactivity, the connection manager 395 can adjust the communicationfrequency with the device 350 to send data that was buffered as a resultof decreased communication frequency to the device 350.

In addition, the proxy server 325 includes priority awareness of variousrequests, transactions, sessions, applications, and/or specific events.Such awareness can be determined by the local proxy on the device 350and provided to the proxy server 325. The priority awareness module 367of the proxy server 325 can generally assess the priority (e.g.,including time-criticality, time-sensitivity, etc.) of various events orapplications; additionally, the priority awareness module 367 can trackpriorities determined by local proxies of devices 350.

In one embodiment, through priority awareness, the connection manager395 can further modify communication frequency (e.g., use or radio ascontrolled by the radio controller 396) of the server 300 with thedevices 350. For example, the server 300 can notify the device 350, thusrequesting use of the radio if it is not already in use when data orupdates of an importance/priority level which meets a criteria becomesavailable to be sent.

In one embodiment, the proxy server 325 can detect multiple occurrencesof events (e.g., transactions, content, data received fromserver/provider 310) and allow the events to accumulate for batchtransfer to device 350. Batch transfer can be cumulated and transfer ofevents can be delayed based on priority awareness and/or useractivity/application behavior awareness as tracked by modules 367 and/or366. For example, batch transfer of multiple events (of a lowerpriority) to the device 350 can be initiated by the batching module 377when an event of a higher priority (meeting a threshold or criteria) isdetected at the server 300. In addition, batch transfer from the server300 can be triggered when the server receives data from the device 350,indicating that the device radio is already in use and is thus on. Inone embodiment, the proxy server 325 can order the each messages/packetsin a batch for transmission based on event/transaction priority suchthat higher priority content can be sent first in case connection islost or the battery dies, etc.

In one embodiment, the server 300 caches data (e.g., as managed by thecaching policy manager 355) such that communication frequency over anetwork (e.g., cellular network) with the device 350 can be modified(e.g., decreased). The data can be cached, for example, in the servercache 335 for subsequent retrieval or batch sending to the device 350 topotentially decrease the need to turn on the device 350 radio. Theserver cache 335 can be partially or wholly internal to the host server300, although in the example of FIG. 3A it is shown as being external tothe host 300. In some instances, the server cache 335 may be the same asand/or integrated in part or in whole with another cache managed byanother entity (e.g., the optional caching proxy server 199 shown in theexample of FIG. 1B), such as being managed by an applicationserver/content provider 310, a network service provider, or anotherthird party.

In one embodiment, content caching is performed locally on the device350 with the assistance of host server 300. For example, proxy server325 in the host server 300 can query the application server/provider 310with requests and monitor changes in responses. When changed or newresponses are detected (e.g., by the new data detector 347), the proxyserver 325 can notify the mobile device 350 such that the local proxy onthe device 350 can make the decision to invalidate (e.g., indicated asout-dated) the relevant cache entries stored as any responses in itslocal cache. Alternatively, the data invalidator module 368 canautomatically instruct the local proxy of the device 350 to invalidatecertain cached data, based on received responses from the applicationserver/provider 310. The cached data is marked as invalid, and can getreplaced or deleted when new content is received from the content server310.

Note that data change can be detected by the detector 347 in one or moreways. For example, the server/provider 310 can notify the host server300 upon a change. The change can also be detected at the host server300 in response to a direct poll of the source server/provider 310. Insome instances, the proxy server 325 can in addition, pre-load the localcache on the device 350 with the new/updated data. This can be performedwhen the host server 300 detects that the radio on the mobile device isalready in use, or when the server 300 has additional content/data to besent to the device 350.

One or more the above mechanisms can be implemented simultaneously oradjusted/configured based on application (e.g., different policies fordifferent servers/providers 310). In some instances, the sourceprovider/server 310 may notify the host 300 for certain types of events(e.g., events meeting a priority threshold level). In addition, theprovider/server 310 may be configured to notify the host 300 at specifictime intervals, regardless of event priority.

In one embodiment, the proxy server 325 of the host 300 canmonitor/track responses received for the data request from the contentsource for changed results prior to returning the result to the mobiledevice, such monitoring may be suitable when data request to the contentsource has yielded same results to be returned to the mobile device,thus preventing network/power consumption from being used when no newchanges are made to a particular requested. The local proxy of thedevice 350 can instruct the proxy server 325 to perform such monitoringor the proxy server 325 can automatically initiate such a process uponreceiving a certain number of the same responses (e.g., or a number ofthe same responses in a period of time) for a particular request.

In one embodiment, the server 300, through the activity/behaviorawareness module 366, is able to identify or detect user activity at adevice that is separate from the mobile device 350. For example, themodule 366 may detect that a user's message inbox (e.g., email or typesof inbox) is being accessed. This can indicate that the user isinteracting with his/her application using a device other than themobile device 350 and may not need frequent updates, if at all.

The server 300, in this instance, can thus decrease the frequency withwhich new or updated content is sent to the mobile device 350, oreliminate all communication for as long as the user is detected to beusing another device for access. Such frequency decrease may beapplication specific (e.g., for the application with which the user isinteracting with on another device), or it may be a general frequencydecrease (E.g., since the user is detected to be interacting with oneserver or one application via another device, he/she could also use itto access other services) to the mobile device 350.

In one embodiment, the host server 300 is able to poll content sources310 on behalf of devices 350 to conserve power or battery consumption ondevices 350. For example, certain applications on the mobile device 350can poll its respective server 310 in a predictable recurring fashion.Such recurrence or other types of application behaviors can be trackedby the activity/behavior module 366 in the proxy controller 365. Thehost server 300 can thus poll content sources 310 for applications onthe mobile device 350 that would otherwise be performed by the device350 through a wireless (e.g., including cellular connectivity). The hostserver can poll the sources 310 for new or changed data by way of theHTTP access engine 345 to establish HTTP connection or by way of radiocontroller 396 to connect to the source 310 over the cellular network.When new or changed data is detected, the new data detector 347 cannotify the device 350 that such data is available and/or provide thenew/changed data to the device 350.

In one embodiment, the connection manager 395 determines that the mobiledevice 350 is unavailable (e.g., the radio is turned off) and utilizesSMS to transmit content to the device 350, for instance, via the SMSCshown in the example of FIG. 1B. SMS is used to transmit invalidationmessages, batches of invalidation messages, or even content in the casewhere the content is small enough to fit into just a few (usually one ortwo) SMS messages. This avoids the need to access the radio channel tosend overhead information. The host server 300 can use SMS for certaintransactions or responses having a priority level above a threshold orotherwise meeting a criteria. The server 300 can also utilize SMS as anout-of-band trigger to maintain or wake-up an IP connection as analternative to maintaining an always-on IP connection.

In one embodiment, the connection manager 395 in the proxy server 325(e.g., the heartbeat manager 398) can generate and/or transmit heartbeatmessages on behalf of connected devices 350 to maintain a backendconnection with a provider 310 for applications running on devices 350.

For example, in the distributed proxy system, local cache on the device350 can prevent any or all heartbeat messages needed to maintain TCP/IPconnections required for applications from being sent over the cellular,or other, network and instead rely on the proxy server 325 on the hostserver 300 to generate and/or send the heartbeat messages to maintain aconnection with the backend (e.g., application server/provider 110 inthe example of FIG. 1A). The proxy server can generate the keep-alive(heartbeat) messages independent of the operations of the local proxy onthe mobile device.

The repositories 312, 314, and/or 316 can additionally store software,descriptive data, images, system information, drivers, and/or any otherdata item utilized by other components of the host server 300 and/or anyother servers for operation. The repositories may be managed by adatabase management system (DBMS), for example, which may be but is notlimited to Oracle, DB2, Microsoft Access, Microsoft SQL Server,PostgreSQL, MySQL, FileMaker, etc.

The repositories can be implemented via object-oriented technologyand/or via text files and can be managed by a distributed databasemanagement system, an object-oriented database management system(OODBMS) (e.g., ConceptBase, FastDB Main Memory Database ManagementSystem, JDOlnstruments, ObjectDB, etc.), an object-relational databasemanagement system (ORDBMS) (e.g., Informix, OpenLink Virtuoso, VMDS,etc.), a file system, and/or any other convenient or known databasemanagement package.

FIG. 3B depicts a block diagram illustrating a further example ofcomponents in the caching policy manager 355 in the cache system shownin the example of FIG. 3A which is capable of caching and adaptingcaching strategies for application (e.g., mobile application) behaviorand/or network conditions.

The caching policy manager 355, in one embodiment, can further include ametadata generator 303, a cache look-up engine 305, an applicationprotocol module 356, a content source monitoring engine 357 having apoll schedule manager 358, a response analyzer 361, and/or an updated ornew content detector 359. In one embodiment, the poll schedule manager358 further includes a host timing simulator 358 a, a long poll requestdetector/manager 358 b, a schedule update engine 358 c, and/or a timeadjustment engine 358 d. The metadata generator 303 and/or the cachelook-up engine 305 can be coupled to the cache 335 (or, server cache)for modification or addition to cache entries or querying thereof.

In one embodiment, the proxy server (e.g., the proxy server 125 or 325of the examples of FIG. 1B and FIG. 3A) can monitor a content source fornew or changed data via the monitoring engine 357. The proxy server, asshown, is an entity external to the mobile device 250 of FIG. 2A-B. Thecontent source (e.g., application server/content provider 110 of FIG.1B) can be one that has been identified to the proxy server (e.g., bythe local proxy) as having content that is being locally cached on amobile device (e.g., mobile device 150 or 250). The content source canbe monitored, for example, by the monitoring engine 357 at a frequencythat is based on polling frequency of the content source at the mobiledevice. The poll schedule can be generated, for example, by the localproxy and sent to the proxy server. The poll frequency can be trackedand/or managed by the poll schedule manager 358.

For example, the proxy server can poll the host (e.g., contentprovider/application server) on behalf of the mobile device and simulatethe polling behavior of the client to the host via the host timingsimulator 358 a. The polling behavior can be simulated to includecharacteristics of a long poll request-response sequences experienced ina persistent connection with the host (e.g., by the long poll requestdetector/manager 358 b). Note that once a polling interval/behavior isset, the local proxy 275 on the device-side and/or the proxy server 325on the server-side can verify whether application and applicationserver/content host behavior match or can be represented by thispredicted pattern. In general, the local proxy and/or the proxy servercan detect deviations and, when appropriate, re-evaluate and compute,determine, or estimate another polling interval.

In one embodiment, the caching policy manager 355 on the server-side ofthe distribute proxy can, in conjunction with or independent of theproxy server 275 on the mobile device, identify or detect long pollrequests. For example, the caching policy manager 355 can determine athreshold value to be used in comparison with a response delay intervaltime (interval time ‘D’ shown in the example timing diagram of FIG.17A-B) in a request-response sequence for an application request toidentify or detect long poll requests, possible long poll requests(e.g., requests for a persistent connection with a host with which theclient communicates including, but not limited to, a long-held HTTPrequest, a persistent connection enabling COMET style push, request forHTTP streaming, etc.), or other requests which can otherwise be treatedas a long poll request.

For example, the threshold value can be determined by the proxy 325using response delay interval times for requests generated byclients/applications across mobile devices which may be serviced bymultiple different cellular or wireless networks. Since the proxy 325resides on host 300 is able to communicate with multiple mobile devicesvia multiple networks, the caching policy manager 355 has access toapplication/client information at a global level which can be used insetting threshold values to categorize and detect long polls.

By tracking response delay interval times across applications acrossdevices over different or same networks, the caching policy manager 355can set one or more threshold values to be used in comparison withresponse delay interval times for long poll detection. Threshold valuesset by the proxy server 325 can be static or dynamic, and can beassociated with conditions and/or a time-to-live (an expirationtime/date in relative or absolute terms).

In addition, the caching policy manager 355 of the proxy 325 can furtherdetermine the threshold value, in whole or in part, based on networkdelays of a given wireless network, networks serviced by a given carrier(service provider), or multiple wireless networks. The proxy 325 canalso determine the threshold value for identification of long pollrequests based on delays of one or more application server/contentprovider (e.g., 110) to which application (e.g., mobile application) ormobile client requests are directed.

The proxy server can detect new or changed data at a monitored contentsource and transmits a message to the mobile device notifying it of sucha change such that the mobile device (or the local proxy on the mobiledevice) can take appropriate action (e.g., to invalidate the cacheelements in the local cache). In some instances, the proxy server (e.g.,the caching policy manager 355) upon detecting new or changed data canalso store the new or changed data in its cache (e.g., the server cache135 or 335 of the examples of FIG. 1B and FIG. 3A, respectively). Thenew/updated data stored in the server cache 335 can be used in someinstances to satisfy content requests at the mobile device; for example,it can be used after the proxy server has notified the mobile device ofthe new/changed content and that the locally cached content has beeninvalidated.

The metadata generator 303, similar to the metadata generator 203 shownin the example of FIG. 2B, can generate metadata for responses cachedfor requests at the mobile device 250. The metadata generator 303 cangenerate metadata for cache entries stored in the server cache 335.Similarly, the cache look-up engine 305 can include the same or similarfunctions are those described for the cache look-up engine 205 shown inthe example of FIG. 2B.

The response analyzer 361 can perform any or all of the functionalitiesrelated to analyzing responses received for requests generated at themobile device 250 in the same or similar fashion to the responseanalyzer 246 d of the local proxy shown in the example of FIG. 2B. Sincethe proxy server 325 is able to receive responses from the applicationserver/content source 310 directed to the mobile device 250, the proxyserver 325 (e.g., the response analyzer 361) can perform similarresponse analysis steps to determine cacheability, as described for theresponse analyzer of the local proxy. Examples of response analysisprocedures are also described in conjunction with the flow charts shownin the examples of FIG. 11-13. The responses can be analyzed in additionto or in lieu of the analysis that can be performed at the local proxy275 on the mobile device 250.

Furthermore, the schedule update engine 358 c can update the pollinginterval of a given application server/content host based on applicationrequest interval changes of the application at the mobile device 250 asdescribed for the schedule update engine in the local proxy 275. Thetime adjustment engine 358 d can set an initial time at which polls ofthe application server/content host is to begin to prevent the servingof out of date content once again before serving fresh content asdescribed for the schedule update engine in the local proxy 275. Boththe schedule updating and the time adjustment algorithms can beperformed in conjunction with or in lieu of the similar processesperformed at the local proxy 275 on the mobile device 250.

FIG. 3C depicts a block diagram illustrating another example ofcomponents in the caching policy manager 355 in the proxy server 375 onthe server-side of the distributed proxy system shown in the example ofFIG. 3A which is capable of managing and detecting cache defeatingmechanisms and monitoring content sources.

The caching policy manager 355, in one embodiment, can further include acache defeating source manager 352, a content source monitoring engine357 having a poll schedule manager 358, and/or an updated or new contentdetector 359. The cache defeating source manager 352 can further includean identifier modifier module 353 and/or an identifier pattern trackingmodule 354.

In one embodiment, the proxy server (e.g., the proxy server 125 or 325of the examples of FIG. 1B and FIG. 3A) can monitor a content source fornew or changed data via the monitoring engine 357. The content source(e.g., application server/content provider 110 of FIG. 1B or 310 of FIG.3A) can be one that has been identified to the proxy server (e.g., bythe local proxy) as having content that is being locally cached on amobile device (e.g., mobile device 150 or 250). The content source 310can be monitored, for example, by the monitoring engine 357 at afrequency that is based on polling frequency of the content source atthe mobile device. The poll schedule can be generated, for example, bythe local proxy and sent to the proxy server 325. The poll frequency canbe tracked and/or managed by the poll schedule manager 358.

In one embodiment, the proxy server 325 uses a normalized identifier ormodified identifier in polling the content source 310 to detect new orchanged data (responses). The normalized identifier or modifiedidentifier can also be used by the proxy server 325 in storing responseson the server cache 335. In general, the normalized or modifiedidentifiers can be used when cache defeat mechanisms are employed forcacheable content. Cache defeat mechanisms can be in the form of achanging parameter in an identifier such as a URI or URL and can includea changing time/data parameter, a randomly varying parameter, or othertypes parameters.

The normalized identifier or modified identifier removes or otherwisereplaces the changing parameter for association with subsequent requestsand identification of associated responses and can also be used to pollthe content source. In one embodiment, the modified identifier isgenerated by the cache defeating source manager 352 (e.g., theidentifier modifier module 353) of the caching policy manager 355 on theproxy server 325 (server-side component of the distributed proxysystem). The modified identifier can utilize a substitute parameter(which is generally static over a period of time) in place of thechanging parameter that is used to defeat cache.

The cache defeating source manager 352 optionally includes theidentifier pattern tracking module 354 to track, store, and monitor thevarious modifications of an identifier or identifiers that addresscontent for one or more content sources (e.g., applicationserver/content host 110 or 310) to continuously verify that the modifiedidentifiers and/or normalized identifiers used by the proxy server 325to poll the content sources work as predicted or intended (e.g., receivethe same responses or responses that are otherwise still relevantcompared to the original, unmodified identifier).

In the event that the pattern tracking module 354 detects a modificationor normalization of an identifier that causes erratic or unpredictablebehavior (e.g., unexpected responses to be sent) on the content source,the tracking module 354 can log the modification and instruct the cachedefeating source manager 352 to generate anothermodification/normalization, or notify the local proxy (e.g., local proxy275) to generate another modification/normalization for use in pollingthe content source. In the alternative or in parallel, the requests fromthe given mobile application/client on the mobile device (e.g., mobiledevice 250) can temporarily be sent across the network to the contentsource for direct responses to be provided to the mobile device and/oruntil a modification of an identifier which works can be generated.

In one embodiment, responses are stored as server cache elements in theserver cache when new or changed data is detected for a response that isalready stored on a local cache (e.g., cache 285) of the mobile device(e.g., mobile device 250). Therefore, the mobile device or local proxy275 can connect to the proxy server 325 to retrieve the new or changeddata for a response to a request which was previously cached locally inthe local cache 285 (now invalid, out-dated, or otherwise determined tobe irrelevant).

The proxy server 325 can detect new or changed data at a monitoredapplication server/content host 310 and transmits a message to themobile device notifying it of such a change such that the mobile device(or the local proxy on the mobile device) can take appropriate action(e.g., to invalidate the cache elements in the local cache). In someinstances, the proxy server (e.g., the caching policy manager 355), upondetecting new or changed data, can also store the new or changed data inits cache (e.g., the server cache 135 or 335 of the examples of FIG. 1Band FIG. 3A, respectively). The updated/new data stored in the servercache can be used, in some instances, to satisfy content requests at themobile device; for example, it can be used after the proxy server hasnotified the mobile device of the new/changed content and that thelocally cached content has been invalidated.

FIG. 3D depicts a block diagram illustrating examples of additionalcomponents in proxy server 325 shown in the example of FIG. 3A which isfurther capable of performing mobile traffic categorization and policyimplementation based on application behavior and/or traffic priority.

In one embodiment of the proxy server 325, the traffic shaping engine375 is further coupled to a traffic analyzer 336 for categorizing mobiletraffic for policy definition and implementation for mobile traffic andtransactions directed to one or more mobile devices (e.g., mobile device250 of FIG. 2A-2D) or to an application server/content host (e.g., 110of FIG. 1A-1B). In general, the proxy server 325 is remote from themobile devices and remote from the host server, as shown in the examplesof FIG. 1A-1B. The proxy server 325 or the host server 300 can monitorthe traffic for multiple mobile devices and is capable of categorizingtraffic and devising traffic policies for different mobile devices.

In addition, the proxy server 325 or host server 300 can operate withmultiple carriers or network operators and can implementcarrier-specific policies relating to categorization of traffic andimplementation of traffic policies for the various categories. Forexample, the traffic analyzer 336 of the proxy server 325 or host server300 can include one or more of, a prioritization engine 341 a, a timecriticality detection engine 341 b, an application state categorizer 341c, and/or an application traffic categorizer 341 d.

Each of these engines or modules can track different criterion for whatis considered priority, time critical, background/foreground, orinteractive/maintenance based on different wireless carriers. Differentcriterion may also exist for different mobile device types (e.g., devicemodel, manufacturer, operating system, etc.). In some instances, theuser of the mobile devices can adjust the settings or criterionregarding traffic category and the proxy server 325 is able to track andimplement these user adjusted/configured settings.

In one embodiment, the traffic analyzer 336 is able to detect,determined, identify, or infer, the activity state of an application onone or more mobile devices (e.g., mobile device 150 or 250) whichtraffic has originated from or is directed to, for example, via theapplication state categorizer 341 c and/or the traffic categorizer 341d. The activity state can be determined based on whether the applicationis in a foreground or background state on one or more of the mobiledevices (via the application state categorizer 341 c) since the trafficfor a foreground application vs. a background application may be handleddifferently to optimize network use.

In the alternate or in combination, the activity state of an applicationcan be determined by the wirelessly connected mobile devices (e.g, viathe application behavior detectors in the local proxies) andcommunicated to the proxy server 325. For example, the activity statecan be determined, detected, identified, or inferred with a level ofcertainty of heuristics, based on the backlight status at mobile devices(e.g., by a backlight detector) or other software agents or hardwaresensors on the mobile device, including but not limited to, resistivesensors, capacitive sensors, ambient light sensors, motion sensors,touch sensors, etc. In general, if the backlight is on, the traffic canbe treated as being or determined to be generated from an applicationthat is active or in the foreground, or the traffic is interactive. Inaddition, if the backlight is on, the traffic can be treated as being ordetermined to be traffic from user interaction or user activity, ortraffic containing data that the user is expecting within some timeframe.

The activity state can be determined from assessing, determining,evaluating, inferring, identifying user activity at the mobile device250 (e.g., via the user activity module 215) and communicated to theproxy server 325. In one embodiment, the activity state is determinedbased on whether the traffic is interactive traffic or maintenancetraffic. Interactive traffic can include transactions from responses andrequests generated directly from user activity/interaction with anapplication and can include content or data that a user is waiting orexpecting to receive. Maintenance traffic may be used to support thefunctionality of an application which is not directly detected by auser. Maintenance traffic can also include actions or transactions thatmay take place in response to a user action, but the user is notactively waiting for or expecting a response.

The time criticality detection engine 341 b can generally determine,identify, infer the time sensitivity of data contained in traffic sentfrom the mobile device 250 or to the mobile device from the host server300 or proxy server 325, or the application server (e.g., appserver/content source 110). For example, time sensitive data caninclude, status updates, stock information updates, IM presenceinformation, email messages or other messages, actions generated frommobile gaming applications, webpage requests, location updates, etc.

Data that is not time sensitive or time critical, by nature of thecontent or request, can include requests to delete messages,mark-as-read or edited actions, application-specific actions such as aadd-friend or delete-friend request, certain types of messages, or otherinformation which does not frequently changing by nature, etc. In someinstances when the data is not time critical, the timing with which toallow the traffic to be sent to a mobile device is based on when thereis additional data that needs to the sent to the same mobile device. Forexample, traffic shaping engine 375 can align the traffic with one ormore subsequent transactions to be sent together in a single power-onevent of the mobile device radio (e.g, using the alignment module 378and/or the batching module 377). The alignment module 378 can also alignpolling requests occurring close in time directed to the same hostserver, since these request are likely to be responded to with the samedata.

In general, whether new or changed data is sent from a host server to amobile device can be determined based on whether an application on themobile device to which the new or changed data is relevant, is runningin a foreground (e.g., by the application state categorizer 341 c), orthe priority or time criticality of the new or changed data. The proxyserver 325 can send the new or changed data to the mobile device if theapplication is in the foreground on the mobile device, or if theapplication is in the foreground and in an active state interacting witha user on the mobile device, and/or whether a user is waiting for aresponse that would be provided in the new or changed data. The proxyserver 325 (or traffic shaping engine 375) can send the new or changeddata that is of a high priority or is time critical.

Similarly, the proxy server 325 (or the traffic shaping engine 375) cansuppressing the sending of the new or changed data if the application isin the background on the mobile device. The proxy server 325 can alsosuppress the sending of the new or changed data if the user is notwaiting for the response provided in the new or changed data; whereinthe suppressing is performed by a proxy server coupled to the hostserver and able to wirelessly connect to the mobile device.

In general, if data, including new or change data is of a low priorityor is not time critical, the proxy server can waiting to transfer thedata until after a time period, or until there is additional data to besent (e.g. via the alignment module 378 and/or the batching module 377).

FIG. 3E depicts a block diagram illustrating examples of additionalcomponents in the traffic shaping engine 375 of the example of FIG. 3Awhich is further capable of aligning data transfer to a mobile orbroadband device, or other recipient, to optimize connectionsestablished for transmission in a wireless network or broadband network.

In one embodiment of the proxy server 325, the traffic shaping engine375 further includes a notification engine 379 and the alignment module378 includes an adjusted poll tracker 378 a and the batching module 377further includes a connection trigger 377 a.

In one embodiment, the proxy server 325 is able to poll distinct hostsservicing various applications (e.g., first and second services) on agiven mobile device at a schedule. The polling schedule can be set bythe local proxy (e.g., proxy 275 of FIG. 2A-2E) and can include assignedpolling intervals for applications on a mobile device (e.g., device 250)which may have been adjusted. The polling schedules can be tracked bythe adjusted poll tracker 378 a in the alignment module 378 of thetraffic shaping engine 375 in the proxy server 325, for example. Theadjusted polling intervals of one service/one application can bedetermined based on the polling interval of another service on themobile device, such that data received at the remote proxy 325 can beprovided to the mobile device in batch, for example, by the batchingmodule 377.

The polling schedule can also include an initial start time (t0) tostart polling on behalf of multiple applications on a given mobiledevice. The initial start time (e.g., a mutual starting point in time)of a first poll of the distinct hosts servicing the first and secondservices can be selected, for example, by the local proxy 275 (e.g.,proxy 275 of FIG. 2A-2E), and in some instances, by the proxy server325. When determined by the local proxy, the local proxy communicatesthe mutual starting point in time for polls to the proxy server 325. Inone embodiment, the mutual starting point in time is set to be in thefuture to compensate for communication delay.

In one embodiment, if a given mobile client/mobile application is not onor active, or if a given mobile device 250 is not connected to thewireless network, the connection trigger 377 a can send a trigger (e.g.,out of band) trigger to the mobile device or the local proxy on themobile device to request that the radio be powered and/or to activateone or more relevant applications. For example, the batching module 377may have batched various content or data to be sent for multipleapplications on a given mobile device and if the mobileclients/applications are not on or active, the connection trigger 377 acan send a trigger requesting the application to activate.Alternatively, the notification engine 379 can send the mobile device250 an indication that there is data ready to be sent, requesting themobile device 250 to power on the radio if currently in off-mode.

Note that the proxy server 325 monitors multiple mobile devices andtracks application characteristics and user behavior/characteristicsacross multiple devices, users, and networks. Thus, the above describedfeatures pertaining to adjusted poll interval trackers, although drawnto an example directed to multiple applications on a given device, notethat the same is tracked for multiple devices, having installed thereonits own other set of applications, for which adjusted poll intervals orpolling schedules are computed based on applications on each mobiledevice by, for example the local proxy residing there on (e.g., thecomponents illustrated in FIG. 2E of a local proxy 275 which may beinstalled on one or more of the multiple mobile devices serviced by theproxy server 325).

Note that since the proxy server 325 manages the traffic to/frommultiple mobile devices, in one network, across networks, in onegeographical locale, across multiple geographical locales, for onenetwork operator, or across multiple network operators, the proxy server325 can align traffic and batch transfer of data based on overview oraggregate data of traffic conditions or network conditions. The proxyserver 325 can prioritize data transfer to mobile devices, for example,when network congestion is detected. For example, the proxy server 325can transfer data to mobile devices where the type or level ofsubscription of the device user, tiered or staggered based on highestpriority of content to be transferred to be the mobile devices (e.g., abatch of data may be transferred first to mobile device A, compared tomobile device B, when the highest priority data for device A is ofhigher priority than device B).

Note that there may be one proxy server 325 for a geographical locale,or for a specific network operator, for a type of web service, or anycombination of the above, for example. Based on the different servicingentities, the proxy server 325 can aggregate different types ofinformation pertaining to network traffic, operator settings,application preferences/requirements, user preferences,subscription-related parameters, various combination of the above can beused by the proxy 325 in optimizing connections need to be establishedby receiving mobile devices. Multiple proxy servers 325 servicingdifferent networks in a geographical locale, different operators canshare traffic, subscription, user, or application level informationthere between, to further facilitate network resource utilization,traffic management, and in some instances to facilitate alignment ofdata transfer to mobile devices.

FIG. 4 depicts another flow diagram illustrating an example process fordistributed content caching between a mobile device and a proxy serverand the distributed management of content caching. As shown herein, thedisclosed technology is a distributed caching model with various aspectsof caching tasks split between the client-side/mobile device side (e.g.,local proxy 275 in the example of FIG. 2) and the server side (e.g.,proxy server 325 of FIG. 3).

In general the device-side responsibilities can include deciding whethera response to a particular request can be and/or should be cached. Thedevice-side of the proxy can make this decision based on information(e.g., timing characteristics, detected pattern, detected pattern withheuristics, indication of predictability or repeatability) collectedfrom/during both request and response and cache it (e.g., storing it ina local cache on the mobile device). The device side can also notify theserver-side in the distributed cache system of the local cache event andnotify it monitor the content source (e.g., application server/contentprovider 110 of FIG. 1B-C).

The device side can further instruct the server side of the distributedproxy to periodically validate the cache response (e.g., by way ofpolling, or sending polling requests to the content source). The deviceside can further decide whether a response to a particular cache requestshould be returned from the local cache (e.g., whether a cache hit isdetected). The decision can be made by the device side (e.g., the localproxy on the device) using information collected from/during requestand/or responses received from the content source.

In general, the server-side responsibilities can include validatingcached responses for relevancy (e.g., determine whether a cachedresponse is still valid or relevant to its associated request). Theserver-side can send the mobile device an invalidation request to notifythe device side when a cached response is detected to be no longer validor no longer relevant (e.g., the server invalidates a given contentsource). The device side then can remove the response from the localcache.

The diagram of FIG. 4 illustrates caching logic processes performed foreach detected or intercepted request (e.g., HTTP request) detected at amobile device (e.g., client-side of the distributed proxy). In step 602,the client-side of the proxy (e.g., local proxy 275) receives a request(from an application (e.g., mobile application) or mobile client). Instep 604, URL is normalized and in step 606 the client-side checks todetermine if the request is cacheable. If the request is determined tobe not cacheable in step 612, the request is sent to the source(application server/content provider) in step 608 and the response isreceived 610 and delivered to the requesting application 622, similar toa request-response sequence without interception by the client sideproxy.

If the request is determined to be cacheable, in step 612, theclient-side looks up the cache to determine whether a cache entry existsfor the current request. If so, in step 624, the client-side candetermine whether the entry is valid and if so, the client side cancheck the request to see if includes a validator (e.g., a modifiedheader or an entity tag) in step 615. For example, the concept ofvalidation is eluded to in section 13.3 of RFC 2616 which describes inpossible types of headers (e.g., eTAG, Modified_Since, must_revlaidate,pragma no_cache) and forms a validating response 632 if so to bedelivered to the requesting application in step 622. If the request doesnot include a validator as determined by step 615, a response is formedfrom the local cache in step 630 and delivered to the requestingapplication in step 622. This validation step can be used for contentthat would otherwise normally be considered un-cacheable.

If, instead, in step 624, the cache entry is found but determined to beno longer valid or invalid, the client side of the proxy sends therequest 616 to the content source (application server/content host) andreceives a response directly from the source in step 618. Similarly, ifin step 612, a cache entry was not found during the look up, the requestis also sent in step 616. Once the response is received, the client sidechecks the response to determine if it is cacheable in step 626. If so,the response is cached in step 620. The client then sends another pollin step 614 and then delivers the response to the requesting applicationin step 622.

FIG. 5 depicts a sequence diagram showing how data requests from amobile device 450 to an application server/content provider 495 in awireless network can be coordinated by a distributed proxy system 460 ina manner such that network and battery resources are conserved throughusing content caching and monitoring performed by the distributed proxysystem 460.

In satisfying application or client requests on a mobile device 450without the distributed proxy system 460, the mobile device 450, or thesoftware widget executing on the device 450, performs a data request 452(e.g., an HTTP GET, POST, or other request) directly to the applicationserver 495 and receives a response 404 directly from the server/provider495. If the data has been updated, the widget 455 on the mobile device450 can refreshes itself to reflect the update and waits for smallperiod of time and initiates another data request to the server/provider495.

In one embodiment, the requesting client or software widget 455 on thedevice 450 can utilize the distributed proxy system 460 in handling thedata request made to server/provider 495. In general, the distributedproxy system 460 can include a local proxy 465 (which is typicallyconsidered a client-side component of the system 460 and can reside onthe mobile device 450), a caching proxy 475 (considered a server-sidecomponent 470 of the system 460 and can reside on the host server 485 orbe wholly or partially external to the host server 485), and a hostserver 485. The local proxy 465 can be connected to the caching proxy475 and host server 485 via any network or combination of networks.

When the distributed proxy system 460 is used for data/applicationrequests, the widget 455 can perform the data request 456 via the localproxy 465. The local proxy 465, can intercept the requests made bydevice applications, and can identify the connection type of the request(e.g., an HTTP get request or other types of requests). The local proxy465 can then query the local cache for any previous information aboutthe request (e.g., to determine whether a locally stored response isavailable and/or still valid). If a locally stored response is notavailable or if there is an invalid response stored, the local proxy 465can update or store information about the request, the time it was made,and any additional data, in the local cache. The information can beupdated for use in potentially satisfying subsequent requests.

The local proxy 465 can then send the request to the host server 485 andthe host server 485 can perform the request 456 and returns the resultsin response 458. The local proxy 465 can store the result and, inaddition, information about the result and returns the result to therequesting widget 455.

In one embodiment, if the same request has occurred multiple times(within a certain time period) and it has often yielded same results,the local proxy 465 can notify 460 the server 485 that the requestshould be monitored (e.g., steps 462 and 464) for result changes priorto returning a result to the local proxy 465 or requesting widget 455.

In one embodiment, if a request is marked for monitoring, the localproxy 465 can now store the results into the local cache. Now, when thedata request 466, for which a locally response is available, is made bythe widget 455 and intercepted at the local proxy 465, the local proxy465 can return the response 468 from the local cache without needing toestablish a connection communication over the wireless network.

In addition, the server proxy performs the requests marked formonitoring 470 to determine whether the response 472 for the givenrequest has changed. In general, the host server 485 can perform thismonitoring independently of the widget 455 or local proxy 465operations. Whenever an unexpected response 472 is received for arequest, the server 485 can notify the local proxy 465 that the responsehas changed (e.g., the invalidate notification in step 474) and that thelocally stored response on the client should be erased or replaced witha new response.

In this case, a subsequent data request 476 by the widget 455 from thedevice 450 results in the data being returned from host server 485(e.g., via the caching proxy 475), and in step 478, the request issatisfied from the caching proxy 475. Thus, through utilizing thedistributed proxy system 460, the wireless (cellular) network isintelligently used when the content/data for the widget or softwareapplication 455 on the mobile device 450 has actually changed. As such,the traffic needed to check for the changes to application data is notperformed over the wireless (cellular) network. This reduces the amountof generated network traffic and shortens the total time and the numberof times the radio module is powered up on the mobile device 450, thusreducing battery consumption and, in addition, frees up networkbandwidth.

FIG. 6 depicts a table 700 showing examples of different traffic orapplication category types which can be used in implementing networkaccess and content delivery policies. For example, traffic/applicationcategories can include interactive or background, whether a user iswaiting for the response, foreground/background application, and whetherthe backlight is on or off.

FIG. 7 depicts a table 800 showing examples of different contentcategory types which can be used in implementing network access andcontent delivery policies. For example, content category types caninclude content of high or low priority, and time critical or non-timecritical content/data.

FIG. 8 depicts an interaction diagram showing how application (e.g.,mobile application) 955 polls having data requests from a mobile deviceto an application server/content provider 995 over a wireless networkcan be can be cached on the local proxy 965 and managed by thedistributed caching system (including local proxy 965 and the hostserver 985 (having server cache 935 or caching proxy server 975)).

In one example, when the mobile application/widget 955 polls anapplication server/provider 932, the poll can locally be intercepted 934on the mobile device by local proxy 965. The local proxy 965 can detectthat the cached content is available for the polled content in therequest and can thus retrieve a response from the local cache to satisfythe intercepted poll 936 without requiring use of wireless networkbandwidth or other wireless network resources. The mobileapplication/widget 955 can subsequently receive a response to the pollfrom a cache entry 938.

In another example, the mobile application widget 955 polls theapplication server/provider 940. The poll is intercepted 942 by thelocal proxy 965 and detects that cache content is unavailable in thelocal cache and decides to set up the polled source for caching 944. Tosatisfy the request, the poll is forwarded to the content source 946.The application server/provider 995 receives the poll request from theapplication and provides a response to satisfy the current request 948.In 950, the application (e.g., mobile application)/widget 955 receivesthe response from the application server/provider to satisfy therequest.

In conjunction, in order to set up content caching, the local proxy 965tracks the polling frequency of the application and can set up a pollingschedule to be sent to the host server 952. The local proxy sends thecache set up to the host server 954. The host server 985 can use thecache set up which includes, for example, an identification of theapplication server/provider to be polled and optionally a pollingschedule 956. The host server 985 can now poll the applicationserver/provider 995 to monitor responses to the request 958 on behalf ofthe mobile device. The application server receives the poll from thehost server and responds 960. The host server 985 determines that thesame response has been received and polls the application server 995according to the specified polling schedule 962. The applicationserver/content provider 995 receives the poll and responds accordingly964.

The host server 985 detects changed or new responses and notifies thelocal proxy 965. The host server 985 can additional store the changed ornew response in the server cache or caching proxy 968. The local proxy965 receives notification from the host server 985 that new or changeddata is now available and can invalidate the affected cache entries 970.The next time the application (e.g., mobile application)/widget 955generates the same request for the same server/content provider 972, thelocal proxy determines that no valid cache entry is available andinstead retrieves a response from the server cache 974, for example,through an HTTP connection. The host server 985 receives the request forthe new response and sends the response back 976 to the local proxy 965.The request is thus satisfied from the server cache or caching proxy 978without the need for the mobile device to utilize its radio or toconsume mobile network bandwidth thus conserving network resources.

Alternatively, when the application (e.g., mobile application) generatesthe same request in step 980, the local proxy 965, in response todetermining that no valid cache entry is available, forwards the poll tothe application server/provider in step 982 over the mobile network. Theapplication server/provider 995 receives the poll and sends the responseback to the mobile device in step 984 over the mobile network. Therequest is thus satisfied from the server/provider using the mobilenetwork in step 986.

Example Signaling or Connection Modeling

FIG. 9 depicts a flow diagram illustrating an example process formodeling signaling of a mobile device (e.g., any wireless device) in amobile network. The operations or steps illustrated with respect to FIG.9 are discussed with performance by a mobile device. However, theoperations or steps may be performed in various embodiments by any ofthe one or more components of the Network optimization architecturediscussed herein. For example, the operations or steps may be performedby a network optimization client proxy of a mobile device (e.g., networkoptimization client proxy 175 of mobile device 150 of FIG. 1A-1), amobile device (e.g., mobile device 150), a network optimization (host)server (e.g., network optimization (host) server 100), one or moreprocessors, and/or other components, modules, engines, or toolsdiscussed herein. Additional or fewer data flow operations are possible.

To begin, at step 1010, the mobile device tracks transactions initiatedby mobile applications executing on the mobile device in the mobilenetwork. At step 1012, the mobile device determines if the transactionscause network signaling requiring a corresponding radio connection. Atstep 1014, the mobile device models the network signaling for the mobiledevice.

FIG. 10 depicts a flow diagram illustrating an example process formodeling signaling of a mobile device (e.g., any wireless device) in amobile network. The operations or steps illustrated with respect to FIG.9 are discussed with performance by a mobile device. However, theoperations or steps may be performed in various embodiments by one ormore components of the Network optimization architecture discussedherein. For example, the operations or steps may be performed by anetwork optimization client proxy of a mobile device (e.g., networkoptimization client proxy 175 of mobile device 150 of FIG. 1A-1), amobile device (e.g., mobile device 150), a network optimization (host)server (e.g., network optimization (host) server 100), one or moreprocessors, and/or other components, modules, engines, or toolsdiscussed herein. Additional or fewer data flow operations are possible.

To begin, at step 1020, the mobile device accesses a radio logassociated with the mobile device. The radio log can indicate a state ofa mobile device radio. At step 1022, the mobile device access a trafficactivity log associated with the mobile device. The traffic activity logcan indicate various traffic metrics measured at multiple measurementpoints in the mobile device. At step 1024, the mobile device calculatesone or more log/reporting data fields based on one or more of the radiolog and the traffic activity log. At step 1026, the mobile device modelsthe network signaling for the mobile device based on the one or morelog/reporting data fields.

Example General Connection and Time Calculations

FIG. 11A-FIG. 16D depict example log/reporting data field calculationsfor use in determining general connection and time calculations. Asdiscussed herein, the various field calculations can be used to modelthe signaling in a mobile network. Importantly, the examplelog/reporting data field calculations discussed below include thefollowing notations:

-   -   Short time stamp form (e.g., 07:26:00.000 is used instead of        full form 2012-10-30 07:26:00.000);    -   Only required fields for calculations are shown in input logs;    -   An example default value for network delay is used (e.g., 15 000        milliseconds);    -   An example default value for request delay is used (e.g., 1 000        milliseconds);    -   An example default value for split ratio is used (e.g., 3000);    -   The terms “dormancy” and “network delay” are used synonymously.

The example connections and time calculations discussed herein areprimarily based on two major data collections: radio up intervals andfiltered netLogs (also referred to as traffic activity logs).

As discussed above, the expanded log/reporting data fields can bedivided into multiple types. For example, the expanded log/reportingdata fields can include a connection flag type and a time connectedcounts type (see, e.g., Appendix A and B). Additionally, the expandedlog/reporting data fields can be divided into several categories asillustrated above in Table 1.

More specifically, FIGS. 11A and 11B illustrate calculation of examplereal (or actual) radio time intervals and corresponding log/reportingdata fields. FIGS. 12A and 12B illustrate calculation of example virtualradio time intervals and corresponding log/reporting data fields. FIGS.13A and 13B illustrate calculation of example simulated radio timeintervals and corresponding log/reporting data fields. FIGS. 14A and 14Billustrate calculation of example simulated virtual radio time intervalsand corresponding log/reporting data fields. FIGS. 15A-15D illustratecalculation of simulated per application radio up intervals. FIGS.16A-16D illustrate calculation of virtual simulated per applicationradio up intervals and corresponding log/reporting data fields.

Example Real (or Actual) Fields Calculations

Referring first to FIGS. 11A and 11B which illustrate an exampleoperation for calculation of a real (or actual) radio up interval andgraphical illustration of an example real (or actual) radio timeinterval, respectively. The real (or actual) fields calculation includescalculation of an actual connection field and an actual time connectedfield.

The actual connection field indicates a real connection that occurs overthe network. The radio log can indicate various states of a mobiledevice radio over a period of time and thus can be used to make theactual connection field calculation. That is, the state of the mobiledevice radio can be used to determine if the mobile device radio is/wasup. For example, in some embodiments, if the current state of radio logindicates that the current state of the mobile device radio is set to aDATA_ACTIVITY_CONNECTED state or a WCDMA_DCH state then the mobiledevice radio is considered to be up (or active). Conversely, if thecurrent state of the mobile device radio is set to aDATA_ACTIVITY_DORMANT state or an IDLE state then the mobile deviceradio is considered to be down (or inactive).

In one embodiment, an actual time field is calculated indicating a totaltime interval during which the radio channel was up for a mobile device.That is, the actual time field indicates the time during which thenetwork channel was used to transfer data (e.g., to or from client). Theactual time can be calculated as the sum of all time intervals betweentwo nearest net log items when the net log items are in the radio upinterval. Importantly, when calculating the actual time field, if aparticular net log item is the first net log item in the log after aradio up log item, then its actual time of connection equals to the sumif two values:

-   -   time interval between this net log item and the nearest net log        after it    -   time interval between radio up and this net log item

As discussed with reference to the example of FIG. 11B, the actualconnection fields and the actual time fields can be calculated by firstreading a radio access log and a traffic activity log (also referred toherein as a net log or network log) associated with a mobile device.Together the relevant portions of the radio access log and the trafficactivity log can be referred to herein as an input log associated with amobile device. In the example of FIG. 11A, table 3 below, indicates therelevant portions of the input net log and input radio log(collectively, input log).

TABLE 3 Input Net log and Radio fields RL1 07:26:00.000data_activity_connected data_activity_dormant Radio up ↑ NL107:26:00.500 32 234  23  42 0 0 mobile_gprs NL2 07:26:20.000 43  23 342424 0 0 mobile_gprs NL3 07:27:00.000 32 234 423 234 0 0 mobile_gprs RL207:30:00.000 data_activity_dormant data_activity_connected Radio down ↓

As indicated above, the input net log and input radio log each includevarious net log items. The net log items are indicated by theconnotation “NLx” while the radio log items are indicated using the“RLx” connotation.

The crcs-analysis core tool such as, for example, log/reporting dataanalysis core 255 a of FIG. 2E or CRSC analysis core 375 a of FIG. 3Eprocess the input log(s) to, for example, calculate one or moreadditional log/reporting data fields based on the one or more input logs(e.g., radio log and the traffic activity log). This process can includeutilizing one or more long poll techniques to split one net log iteminto two or more net log items. Use of the one of more long polltechniques is illustrated and discussed in greater detail with referenceto FIGS. 19A and 19B. As shown in this example, the crcs-analysis coretool calculates an actual connection field (or flag) and actual timefield for each net log item. An example output table 4 is illustratedbelow.

TABLE 4 Output Net log fields Actual Actual TimeStamp connection TimeNL1 07:26:00.500 1  20 000 NL2 07:26:20.000 0  40 000 NL3 07:27:00.000 0180 000 TOTAL 1 240 000

In this example, RL1 is radio up log because its “state” field isdata_activity_connected and “prev_state” is data_activity_dormant.Similarly, RL2 is radio down log because its “state” field isdata_activity_dormant and “prev_state” is data_activity_connected.Accordingly, the actual radio up interval is:07:30:00.000-07:26:00.000=4 min=240 sec=240 000 ms.

With respect to the actual connection calculation, NL1 makes an actualconnection because it is first net log after the radio up so this netlog starts a new connection. NL2 and NL3 occurred when radio was alreadyrisen up, so they don't start a new connection. Therefore, actualconnection NL1=1; actual connection NL2=0; and actual connection NL3=0.With respect to the actual time calculation:

-   -   actual time NL1=[RL1, NL1]+[NL1, NL2];    -   actual time NL2=[NL2, NL3]; and    -   actual time NL3=[NL3, RL2].        where,    -   [RL1, NL1] is time interval between radio log item RL1 and net        log item NL1;    -   [NL1, NL2] is the same for net log item NL1 and net log item        NL2;    -   [NL2, NL3] is the same for NL2 and NL3;    -   [NL3, RL2] is time interval between net log item NL3 and radio        log item RL2;

Also,

-   -   [RL1, NL1]=07:26:00.500−07:26:00.000=0 500    -   [NL1, NL2]=07:26:20.000−07:26:00.500=19 500    -   [NL2, NL3]=07:27:00.000−07:26:20.000=20 000    -   [NL3, RL2]=07:30:00.000−07:27:00.000=180 000

Thus,

-   -   actual time        NL1=[07:26:00.500−07:26:00.000]+[07:26:20.000−07:26:00.500]=500+19        500=20 000;    -   actual time NL2=07:27:00.000−07:26:20.000=40 000;    -   actual time NL3=07:30:00.000−07:27:00.000=180 000;

Example Virtual Fields Calculations

Referring next to FIG. 12A and FIG. 12B which illustrate an exampleoperation for calculation of a virtual radio up interval and graphicalillustration of an example virtual radio up interval, respectively. Morespecifically, the virtual fields calculation includes calculation of avirtual connection field and a virtual time connected field.

As discussed above, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Forexample, the crcs-analysis core tool or module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can calculate the virtual radio up intervals. Thecalculation can include calculating one or more additional log/reportingdata fields based on one or more input logs (e.g., radio log and atraffic activity log including cache hit information).

More specifically, as shown in the example of FIG. 12B, thecrcs-analysis core tool or module utilizes the radio Logs and thecacheHit netLogs to calculate the virtual radio up intervals. Thevirtual fields illustrate which connections would happen ‘but for’ theNetwork optimization client on mobile device. FIG. 12A, illustrates anexample architecture for calculation of virtual fields.

The virtual connection fields indicate a virtual connection that is madeeither through cache (no radio up) or through a real (actual)connection. That is, the virtual connections illustrate what connectionswould occur if no Network optimization client was operating on themobile device. Similarly, the virtual time field indicates a timeinterval during which the radio channel would be up if no Networkoptimization client was installed on operating on the mobile device.Accordingly, the total virtual time is always equal to or greater thanthe actual time calculated with respect to the example of FIG. 11A andFIG. 11B.

With reference to the example of FIG. 12A and FIG. 12B, the table 5below indicates the relevant portions of the example input net log andinput radio log (collectively, input log).

TABLE 5 Input Net log fields RL1 07:26:00.000 data_activity_connecteddata_activity_dormant Virtual Radio up ↑ NL1 07:26:00.500  0  0 0 0 323 653  mobile_gprs NL2 07:26:20.000  0  0 0 0 23  432  mobile_gprs NL307:27:00.100 32 234 423  234  0 0 mobile_gprs RL2 07:30:00.000data_activity_dormant data_activity_connected Virtual Radio down ↓ NL407:31:00.000 45  34 0 0 0 0 mobile_gprs Virtual radio up ↑ NL507:31:01.500 45 234 0 0 0 0 mobile_gprs DL 07:31:16.500 No this recordin input. It is only for illustrating Virtual Radio purpose here. down ↓after dormancy (network) delay of 15 sec

In some embodiments, the DL record is not a part of (an item in) theinput log. As indicated above, the input net log and input radio logeach include various net log items. As discussed herein, the net logitems are indicated by the connotation “NLx” while the radio log itemsare indicated using the “RLx” connotation.

As shown in this example, the crcs-analysis core tool calculates anactual connection field (or flag) and actual time field for each net logitem. An example output table 6 is illustrated below.

TABLE 6 Output Net log fields Virtual Virtual TimeStamp connection TimeNL1 07:26:00.500 1 20 000 NL2 07:26:20.000 0 40 000 NL3 07:27:00.000 0180 000  NL4 07:31:00.000 1  1 500 NL5 07:31:01.500 0 15 000 TOTAL 2 256500 

In this example, RL1 is calculated to be the first virtual radio up logitem or entry because real radio up actually occurs. RL2 is calculatedto be the first radio down log because real radio down actually occurs.NL4 is calculated to be the second virtual radio up log because“CLIENT_BYTES_IN” or “CLIENT_BYTES_OUT” are greater than zero. That is,at NL4 data was transferred between network optimization client and thenetwork optimization server. DL is calculated to be the second radiodown log because exactly at DL, the network delay ends. Also, in thisexample, NL5 is not calculated to be a virtual radio up in the logbecause the time interval between NL4 and NL5 is less than dormancy.

With respect to the virtual connection field calculation, NL1 makes avirtual connection because it makes actual connection. NL4 makes avirtual connection because it makes virtual radio up. Therefore:

-   -   Virtual connection NL1=1    -   Virtual connection NL2=0    -   Virtual connection NL3=0    -   Virtual connection NL4=1    -   Virtual connection NL5=0        With respect to the virtual time field calculations:    -   Virtual time NL1=Actual time NL1    -   Virtual time NL2=Actual time NL2    -   Virtual time NL3=Actual time NL3    -   Virtual time NL4=[NL4, NL5]    -   Virtual time NL5=[NL5, DL]        where,    -   [NL4, NL5] is time interval between net log item NL4 and net log        item NL5    -   [NL5, DL] is time interval between net log item NL5 and virtual        radio down item DL and,    -   [NL4, NL5]=07:31:01.500−07:31:00.000=1 500    -   [NL1, NL2]=07:31:16.500−07:31:01.500=15 000        Here DL is time when network delay happens starting from virtual        radio up. Thus,    -   Virtual time NL1=20 000    -   Virtual time NL2=40 000    -   Virtual time NL3=180 000    -   Virtual time NL4=1 500    -   Virtual time NL5=15 000

Therefore,

-   -   Total Virtual Time=Total Actual Time+dormancy (network delay)    -   Total Virtual Time=240 000+16 500=256 500    -   Virtual time NL1=20 000    -   Virtual time NL2=40 000    -   Virtual time NL3=180 000    -   Virtual time NL4=1 500    -   Virtual time NL5=15 000

Example Simulated Fields Calculations

Referring next to FIG. 13A and FIG. 13B which illustrate exampleoperation for calculation of a simulated radio up interval and graphicalillustration of an example simulated radio up interval, respectively.More specifically, the simulated fields calculations includecalculations of simulated connection fields and a simulated timeconnected fields.

In one embodiment, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Forexample, the crcs-analysis core tool or module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can calculate simulated radio up intervals. Thecalculation can include calculating one or more additional log/reportingdata fields based on one or more input logs (e.g., traffic activity logincluding network hit information).

More specifically, as shown in the example of FIG. 13B, thecrcs-analysis core tool or module utilizes the networkHit netLogs tocalculate the simulated radio up intervals.

To calculate simulated fields one the system assumes that allapplications in mobile device use Network optimization client and thereis no application that can start connection without Networkoptimization. Radio log for that situation is called simulated radiolog. The simulated connection field indicates a connection that wouldhappen through network if there were no other application on phoneinstead of those which are under control of Network optimization client.Similarly, the simulated time fields indicate the time of connectionthat would happen through network if there were no other application onphone instead of those which are under control of Network optimizationclient.

With reference to the example of FIG. 13A and FIG. 13B, the table 7below indicates the relevant portions of the input traffic activity logincluding network hits. In some embodiments, the real (actual) radiologs can be ignored when calculating the simulated radio up intervals.Accordingly, the real (actual) radio log items are not shown in theinput table data below.

TABLE 7 Input Net log fields NL1 07:26:00.500 32 234  23  42 0 0mobile_gprs Simulated Radio up ↑ DL1 07:26:15.500 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL2 07:26:20.000 43  23 342 4240 0 mobile_gprs Simulated Radio up ↑ DL2 07:26:35.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL3 07:27:00.000 32 234 423 2340 0 mobile_gprs Simulated Radio up ↑ DL3 07:27:15.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec

In this example, the DL1, DL2, and DL3 records (or items) are notinputs. Rather, the records are illustrated for clarity of descriptionpurposes. As discussed herein, the net log items are indicated by theconnotation “NLx.”

The crcs-analysis core tool such as, for example, log/reporting dataanalysis core 255 a of FIG. 2E or CRSC analysis core 375 a of FIG. 3Eprocesses the input log(s) to, for example, calculate one or moreadditional log/reporting data fields based on the one or more input logs(e.g., the traffic activity log). This process can include utilizing oneor more long poll techniques to split one net log item into two or morenet log items. This process is illustrated and discussed in greaterdetail with reference to FIG. 19A. As shown in this example, thecrcs-analysis core tool calculates simulated connection fields (orflags) and simulated time fields. An example output table 8 illustratingoutput net log fields is illustrated below.

TABLE 8 Output Net log fields Simulated Simulated TimeStamp connectionTime NL1 07:26:00.500 1 15 000 NL2 07:26:20.000 1 15 000 NL307:27:00.000 1 15 000 TOTAL 3 45 000

In this example, NL1 is the first simulated radio up log because net logitem starts here. DL1 is the first simulated radio down log becauseexactly at that time network delay ends. NL2 is the second simulatedradio up log for the same reason as NL1. DL2 is the second simulatedradio down for the same reason as RL1. NL3 is the third simulated radioup log for the same reason as NL1. DL3 is the third simulated radio downlog for the same reason as RL1.

With respect to the simulated connection fields calculations, NL1, NL2,and NL3 make a simulated connection because each causes a simulatedradio up even. Therefore,

-   -   Simulated connection NL1=1;    -   Simulated connection NL2=1;    -   Simulated connection NL3=1.

With respect to the simulated time fields calculations, if the timeinterval between two adjacent net log items is greater than dormancy,then the first net log item will have simulated time equal to dormancy.Otherwise, the time interval between the two adjacent net log items willbe the actual time between the net log items. An example is illustratedin FIG. 13C.

Therefore, in the example of FIG. 13A-13C

-   -   Simulated time NL1=dormancy (network delay)=15 000    -   Simulated time NL2=dormancy (network delay)=15 000    -   Simulated time NL3=dormancy (network delay)=15 000

Example Virtual Simulated Fields Calculations

FIGS. 14A and 14B illustrate an example architecture for calculation ofa virtual simulated radio up interval and illustration of an examplevirtual simulated radio time interval, respectively. More specifically,the virtual simulated fields calculation described below incudescalculation of a virtual simulated field and a virtual simulated timeconnected field.

In one embodiment, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Forexample, the crcs-analysis core tool or module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can calculate simulated virtual radio up intervals.The calculation can include calculating one or more additionallog/reporting data fields based on one or more input logs (e.g., radiolog and a traffic activity log including network hit and cache hitinformation).

More specifically, as shown in the example of FIG. 14B, thecrcs-analysis core tool or module utilizes a networkHit and a cacheHitnetLogs to calculate the simulated virtual radio up intervals. Asdiscussed herein, the radio up (or active) intervals

The simulated virtual fields indicate the connections that happen in asimulated environment in which all applications on the mobile devicethat normally use the Network optimization client are simulated butthere is no Network optimization on the mobile device.

The simulated virtual connection fields indicate the connection(s) thatwould occur through the network in a simulated environment in which allapplications on the mobile device that normally use the Networkoptimization client are simulated but there is no Network optimizationon the mobile device. Similarly, the simulated virtual time fieldsindicate the time connected from the connections that would occurthrough network in a simulated environment in which all applications onthe mobile device that normally use the Network optimization client aresimulated but there is no Network optimization on the mobile device.

With reference to the example of FIG. 14A and FIG. 14B, the table 9below indicates the relevant portions of the input log including networkhits. In a simulated environment in which all applications on the mobiledevice that normally use the Network optimization client are simulatedbut there is no Network optimization on the mobile device.

TABLE 9 Input Net log fields NL1 07:26:00.500 32 234  23  42 0 0mobile_gprs Simulated Radio up ↑ DL1 07:26:16.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL2 07:26:20.000 43  23 342 4240 0 mobile_gprs Simulated Radio up ↑ DL2 07:26:35.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL3 07:27:00.000 32 234 423 2340 0 mobile_gprs Simulated Radio up ↑ DL3 07:27:15.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL4 07:31:00.000 45  34  0  0 00 mobile_gprs Simulated Virtual radio up ↑ NL5 07:31:01.500 45 234  0  00 0 mobile_gprs DL4 07:32:16.500 No this record in input. It is only forillustrating Simulated Virtual purpose here. Radio down ↓ after dormancy(network) delay of 15 sec

In this example, the DL1, DL2, and DL3 records (or items) are notinputs. Rather, these records are illustrated for clarity of descriptionpurposes. As discussed herein, the net log items are indicated by theconnotation “NLx.”

The crcs-analysis core tool such as, for example, log/reporting dataanalysis core 255 a of FIG. 2E or CRSC analysis core 375 a of FIG. 3Eprocesses the input log(s) to, for example, calculate one or moreadditional log/reporting data fields based on the one or more input logs(e.g., the traffic activity log). This process can include utilizing oneor more long poll techniques to split one net log item into two or morenet log items. This process is illustrated and discussed in greaterdetail with reference to FIG. 19A. As shown in this example, thecrcs-analysis core tool calculates simulated connection fields (orflags) and simulated time fields. An example output table 10illustrating output net log fields is illustrated below.

TABLE 10 Output Net log fields Simulated virtual Simulated TimeStampconnection virtual Time NL1 07:26:00.500 1 15 000 NL2 07:26:20.000 1 15000 NL3 07:27:00.000 1 15 000 NL4 07:31:00.000 1  1 500 NL5 07:31:01.5000 15 000 TOTAL 4 61 500

In this example, NL4 is the fourth simulated virtual radio up logbecause “CLIENT_BYTES_IN” or “CLIENT_BYTES_OUT” are greater than zeroindicating that data was transferred between network optimization clientand network optimization server. Note that NL5 is not a simulatedvirtual radio up log item because time interval between NL4 and NL5 isless than dormancy.

Example Simulated Per Application Fields Calculations

FIGS. 15A and 15B illustrate an example architecture for calculation ofsimulated radio up intervals and illustration of an example simulatedradio time intervals, respectively. More specifically, the simulatedfields calculation described below incudes calculation of a virtualsimulated field and a virtual simulated time connected field on a perapplication basis.

In one embodiment, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Forexample, the crcs-analysis core tool or module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can calculate simulated radio up intervals on a perapplication basis. The calculation can include calculating one or moreadditional log/reporting data fields based on one or more input logs(e.g., traffic activity log including network hit information).

More specifically, as shown in the example of FIG. 15B, thecrcs-analysis core tool or module utilizes the netLogs of certainapplications to calculate simulated per application radio up intervals.In the example of FIG. 15B, netlog NL1 is associated with a firstapplication #1 and netlogs NL2 and NL3 are associated with a secondapplication #2.

In the example of FIG. 15C, the simulated radio up interval per thefirst application #1 is illustrated. Similarly, the example of FIG. 15Dillustrates the simulated radio up intervals per the second application#2.

The simulated per application fields indicate connections that happen ina simulated environment in which all applications on the mobile devicethat normally use the Network optimization client are simulated butthere is no Network optimization on the mobile device. To calculatesimulated per application fields the system contemplates only oneapplication (e.g., Application #1) on the mobile device. The oneapplication (e.g., Application #1) utilizes the Network optimizationclient and there is no application that can start connection withoutNetwork optimization. The radio log for this situation is calledsimulated per application radio log.

The simulated per application connection is a connection that wouldhappen through the network if there were one application installed on amobile device (under the control of Network optimization client) and noother applications on phone. Similarly, the simulated per applicationtime is time of connection that would happen through network if therewere one application installed on the mobile device (under control ofNetwork optimization client) and no other applications on phone.

With reference to the example of FIG. 15A and FIG. 15B, the table 11below indicates the relevant portions of the input log including networkhits. While calculating simulated radio up intervals we ignore real(actual) radio logs, that is why they are not shown in input data. Seeexample in table 11.

TABLE 11 Input Net log fields NL1 07:26:00.500 32 234  23  42 0 0mobile_gprs App1 Simulated Radio up ↑ DL1 07:26:15.500 No this record ininput. It is only for illustrating Simulated Radio down purpose here. ↓after dormancy (network) delay of 15 sec NL2 07:26:20.000 43  23 342 4240 0 mobile_gprs App2 Simulated Radio up ↑ DL2 07:26:35.000 No thisrecord in input. It is only for illustrating Simulated Radio downpurpose here. ↓ after dormancy (network) delay of 15 sec NL307:27:00.000 32 234 423 234 0 0 mobile_gprs App2 Simulated Radio up ↑DL3 07:27:15.000 No this record in input. It is only for illustratingSimulated Radio down purpose here. ↓ after dormancy (network) delay of15 sec

In this example, the DL1, DL2, and DL3 records (or items) are notinputs. Rather, these records are illustrated for clarity of descriptionpurposes. As discussed herein, the net log items are indicated by theconnotation “NLx.”

The crcs-analysis core tool such as, for example, log/reporting dataanalysis core 255 a of FIG. 2E or CRSC analysis core 375 a of FIG. 3Eprocesses the input log(s) to, for example, calculate one or moreadditional log/reporting data fields based on the one or more input logs(e.g., the traffic activity log). This process can include utilizing oneor more long poll techniques to split one net log item into two or morenet log items. This process is illustrated and discussed in greaterdetail with reference to FIG. 19A. As shown in this example, thecrcs-analysis core tool calculates simulated per application connectionfields (or flags) and simulated per application time fields. An exampleoutput table 12 illustrating output net log fields is illustrated below.

TABLE 12 Output Net log fields Simulated per Simulated per TimeStamp Appconnection App Time NL1 07:26:00.500 1 15 000 NL2 07:26:20.000 1 15 000NL3 07:27:00.000 1 15 000 TOTAL 3 45 000

In this example, NL1 is the first simulated radio up log because net logitem starts here. DL1 is the first simulated radio down log becauseexactly at that time network delay ends. NL2 is the second simulatedradio up log for the same reason as NL1. DL2 is the second simulatedradio down for the same reason as RL1. NL3 is the third simulated radioup log for the same reason as NL1. DL3 is the third simulated radio downlog for the same reason as RL1.

With respect to the simulated per application connection fields, NL1,NL2, and NL3 make a simulated connection because each causes a simulatedradio up event. Therefore,

-   -   Simulated connection NL1=1    -   Simulated connection NL2=1    -   Simulated connection NL3=1        and,    -   Simulated time NL1=dormancy (network delay)=15 000    -   Simulated time NL2=dormancy (network delay)=15 000    -   Simulated time NL3=dormancy (network delay)=15 000

Example Virtual Simulated Per Application Fields Calculations

FIGS. 16A and 16B illustrate an example architecture for calculation ofa virtual simulated per application radio up interval and illustrationof an example virtual simulated per application radio time interval,respectively. More specifically, the virtual simulated per applicationfields calculations described below include calculation of a virtualsimulated per application field and a virtual simulated time connectedper application field.

In one embodiment, a crcs-analysis core tool or module (not shown) cancalculate expanded log/reporting data fields that are maintained andutilized by the crcs-analysis core tool to model signaling of a mobiledevice in a mobile network. More specifically, the crcs-analysis coretool can model the effects of the Network optimization architecture(e.g., the distributed caching techniques including the SignalOptimization and Extended Caching techniques discussed herein). Forexample, the crcs-analysis core tool or module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can calculate simulated virtual radio up intervals.The calculation can include calculating one or more additionallog/reporting data fields based on one or more input logs (e.g., radiolog and a traffic activity log including network hit and cache hitinformation).

More specifically, as shown in the example of FIG. 16B, thecrcs-analysis core tool or module utilizes a networkHit and a cacheHitnetLogs to calculate the simulated virtual radio up intervals. Asdiscussed herein, the radio up intervals indicate a period of timeduring which the mobile device radio is active.

In the example of FIG. 16C, the simulated virtual radio up intervalassociated with the first application #1 is illustrated. Similarly, theexample of FIG. 16D illustrates the simulated virtual radio up intervalsassociated with the second application #2.

The simulated virtual per application fields indicate the connectionsthat happen in a simulated environment in which a single application onthe mobile device that normally uses the Network optimization client issimulated but there is no Network optimization on the mobile device.

The simulated virtual per application connection fields indicate theconnection(s) that would occur through the network in a simulatedenvironment in which a single application on the mobile device thatnormally uses the Network optimization client is simulated but there isno Network optimization on the mobile device. Similarly, the simulatedvirtual time fields indicate the time connected from the connectionsthat would occur through network in a simulated environment in which asingle application on the mobile device that normally uses the Networkoptimization client is simulated but there is no Network optimization onthe mobile device.

With reference to the example of FIG. 16A and FIG. 16B, the table 13below indicates the relevant portions of the input log including networkhits. In a simulated environment in which all applications on the mobiledevice that normally use the Network optimization client are simulatedbut there is no Network optimization on the mobile device.

TABLE 13 Input Net log fields NL1 07:26:00.500 32 234  23  42 0 0mobile_gprs App1 Simulated Radio up↑ DL1 07:26:16.000 No this record ininput. It is only for illustrating Simulated Radio purpose here. down ↓after dormancy (network) delay of 15 sec NL2 07:26:20.000 43  23 342 4240 0 mobile_gprs App2 Simulated Radio up↑ DL2 07:26:35.000 No this recordin input. It is only for illustrating Simulated Radio purpose here. down↓ after dormancy (network) delay of 15 sec NL3 07:27:00.000 32 234 423234 0 0 mobile_gprs App2 Simulated Radio up ↑ DL3 07:27:15.000 No thisrecord in input. It is only for illustrating Simulated Radio purposehere. down ↓ after dormancy (network) delay of 15 sec NL4 07:31:00.00045  34  0  0 0 0 mobile_gprs App2 Simulated Virtual radio up ↑ NL507:31:01.500 45 234  0  0 0 0 mobile_gprs App2 DL4 07:32:16.500 No thisrecord in input. It is only for illustrating Simulated Virtual purposehere. Radio down ↓ after dormancy (network) delay of 15 sec

In this example, DL1, DL2, and DL3 records (or items) are not inputs.Rather, these records are illustrated for clarity of descriptionpurposes. As discussed herein, the net log items are indicated by theconnotation “NLx.”

The crcs-analysis core tool such as, for example, log/reporting dataanalysis core 255 a of FIG. 2E or CRSC analysis core 375 a of FIG. 3Eprocesses the input log(s) to, for example, calculate one or moreadditional log/reporting data fields based on the one or more input logs(e.g., the traffic activity log). This process can include utilizing oneor more long poll techniques to split one net log item into two or morenet log items. This process is illustrated and discussed in greaterdetail with reference to FIG. 19A. As shown in this example, thecrcs-analysis core tool calculates simulated per application connectionfields (or flags) and simulated per application time fields. An exampleoutput table 14 illustrating output net log fields is illustrated below.

TABLE 14 Output Net log fields Simulated Simulated virtual per virtualper TimeStamp app connection app Time NL1 07:26:00.500 1 15 000 NL207:26:20.000 1 15 000 NL3 07:27:00.000 1 15 000 NL4 07:31:00.000 1  1500 NL5 07:31:01.500 0 15 000 TOTAL 4 61 500

Example Saved Values

As discussed herein, the various field calculations can be used to modelthe signaling in a mobile network. For example, the modeling can includecalculating a saved connections and a saved time. The saved connectionsindicate the amount, number, or quantity of connections that were savedas a result of utilizing the Network optimization architecture. In oneembodiment, the saved connections can be modeled as follows:

-   -   Saved connection=Virtual connection−Actual connection,    -   Saved simulated connection=Simulated virtual        connection−Simulated connection,    -   Saved simulated per app connection=Simulated virtual per app        connection−Simulated per app connection,    -   Saved simulated per host connection=Simulated virtual per host        connection−Simulated per host connection.

Similarly, the saved time is time interval of connection time that wassaved as a result of utilizing the Network optimization architecture. Inone embodiment, the saved time can be modeled as follows:

-   -   Saved time=Virtual time−Actual time,    -   Saved simulated time=Simulated virtual time−Simulated time,    -   Saved simulated per app time=Simulated virtual per app        time−Simulated per app time,    -   Saved simulated per host time=Simulated virtual per host        time−Simulated per host time.

Connection Flags and Time

FIG. 17A illustrates an example of calculating the connection flags andconnection time intervals discussed above. Connection flags indicatewhether a particular netLog caused radio up. In one embodiment, todetermine the connection flag for each radio up interval the closestnetLog to radio up log record is marked with connection flag.Importantly, only netlogs that are in Request Delay neighborhood toRadio Up log are marked with a connection flag.

FIG. 17B illustrates an example radio up interval. The radio up intervalmay be calculated, maintained, and/or otherwise obtained to calculatethe time connected indicate impact of a particular netLog on radio uptime. For each radio up interval, the radio up time equals to sum oftime connected values for netLogs that belong to that particularinterval.

Network Hits

A network hit is start point of data transfer at network optimizationserver side. In one embodiment, a net log item is considered a networkhit when at least one of these conditions of its associated fields aretrue:

-   -   SERVER_BYTES_IN>0;    -   SERVER_BYTES_OUT>0;    -   OPERATION=radio_up; and,    -   OPERATION !=proxy_tc_handshake

Cache Hits

A cache hit is start point of data transfer in cache. In one embodiment,a net log item is considered a cache hit when it is not net log hit andat least one of these conditions of its associated fields are true:

-   -   CLIENT_BYTES_IN>0    -   CLIENT_BYTES_OUT>0    -   OPERATION=deferred_app_close; and,    -   OPERATION !=proxy_https_handshake

Detailed Example Calculation

The following example illustrates another field calculation. To begin,suppose the following input netlog and radio log:

TABLE 15 Input Net log fields Client Client Server Server Cache Cache #Timestamp bytes in bytes out bytes in bytes out bytes in bytes inInterface 1 07:26:00.000 111 111 0 0 0 0 mobile_gprs 2 07:26:20.000 111111 0 0 0 0 mobile_gprs 3 07:27:00.100 32 234 423 234 0 0 mobile_gprs 407:29:00.000 432 63 476 73 0 0 mobile_gprs 5 07:29:00.500 234 32 23 2610 0 mobile_gprs 6 07:31:00.000 111 111 0 0 0 0 mobile_gprs

TABLE 16 Input Radio log fields # Timestamp State Prev state 107:27:00.000 data_activity_connected data_activity_dormant 207:30:00.000 data_activity_dormant data_activity_connected

FIG. 18 depicts an example scheme illustrating logs over a period oftime. For example, assume

-   -   t1=(07:26:20.000−07:26:00.000)=20 000 ms,    -   t2=(07:27:00.000−07:26:20.000)=40 000 ms,    -   t3=(07:27:00.100−07:27:00.000)=100 ms,    -   t4=(07:29:00.000−07:27:00.100)=119 000 ms,    -   t5=(07:29:00.500−07:29:00.000)=500 ms,    -   t6=(07:30:00.000−07:29:00.500)=59 500 ms,    -   t7=(07:31:00.000−07:30:00.000)=60 000 ms, and    -   network delay=15 sec,        thus,    -   t1, (t2+t3), t4, (t6+t7)>network delay; and    -   t5<network delay;    -   t3<request delay.

Table 17, below, illustrates the results of the calculation

TABLE 17 Result calculation Parameter, log-item 1 2 3 4 5 6 Actual conn0 0 1 0 0 0 Virtual conn 1 1 1 0 0 1 Actual time 0 0 t3 + t4 t5 t6 0Virtual time Network Network t3 + t4 t5 t6 Network delay delay delay

Table 18 indicates the output net log description. Note that the outputtime intervals are in milliseconds.

TABLE 18 output net log Actual Virtual Actual Virtual Saved Saved #Timestamp conn conn time time conn time 1 07:26:00.000 0 1 0 15000 115000 2 07:26:20.000 0 1 0 15000 1 15000 3 07:28:00.000 1 1 120000120000 0 0 4 07:29:00.000 0 0 500 500 0 0 5 07:29:01.000 0 0 59500 595000 0 6 07:31:00.000 0 1 0 15000 1 15000

Example Long Poll Procedure

FIG. 19A graphically illustrates a long poll procedure for splitting onenetlog item into two netlog items, according to an embodiment. Morespecifically, the Long poll procedure described herein is the process ofsplitting one netlog item into two netlog items. In one embodiment, theconditions for performing a long poll procedure for netlog item are:

-   -   RESPONSE_TIME is greater than or equals to network delay        (default, e.g., 15 000 ms);    -   Netlog item is network hit or cache hit;    -   Value of SERVER_BYTES_IN/RESPONSE_TIME is less than or equals to        split ratio (default, e.g., 3 000 ms).

FIG. 19B graphically illustrates the conditions which must be true inorder for the netlog to be split in two parts. More specifically, if thevalue of SERVER_BYTES_IN/RESPONSE_TIME is less than or equals to splitratio (default, e.g., 3 000 ms) then the netlog can be split in twoparts. The table 19 below illustrates the field modifications occurringas a result of performing the long poll procedure.

TABLE 19 fields changing after long poll procedure Source netlog Netlogafter splitting Added netlog TMESTAMP => TIMESTAMP TIMESTAMP +RESPONSE_TIME CLIENT_BYTES_IN CLIENT_BYTES_IN 0 CLIENT_BYTES_OUT 0CLIENT_BYTES_OUT SERVER_BYTES_IN 0 SERVER_BYTES_IN SERVER_BYTES_OUTSERVER_BYTES_OUT 0 CACHED_BYTES_IN 0 CACHED_BYTES_IN CACHED_BYTES_OUT 0CACHED_BYTES_OUT RESPONSE_TIME 0 0 Other fields The same as in sourceThe same as in source

Example input and output net logs, pre- and post-split, respectively,are illustrated below.

TABLE 20 Input Net log and Radio fields RL1 07:26:00.000data_activity_connected data_activity_dormant Radio up ↑ NL107:26:00.500 32 234 23 42 0 0 mobile_gprs 27 RL2 07:30:00.000data_activity_dormant data_activity_connected Radio down ↓

TABLE 21 Output Net log and Radio fields RL1 07:26:00.000data_activity_connected data_activity_dormant Radio up ↑ NL107:26:00.500 32  0  0 42 0 0 mobile_gprs 0 NL2 07:26:27.500  0 234 23  00 0 mobile_gprs 0 RL2 07:30:00.000 data_activity_dormantdata_activity_connected Radio down ↓

Example Log Preprocessing

In some embodiments, log preprocessing is performed before the data inthe calculated fields is populated. For example, the followingprocedures can be performed:

-   -   If        -   Operation is PROXY_HTTPS_HANDSHAKE; and        -   CLIENT_BYTES_IN>0 or CLIENT_BYTES_OUT>0; and        -   SERVER_BYTES_IN>0 or SERVER_BYTES_OUT>0;    -   Then replace CLIENT_BYTES_OUT with SERVER_BYTES_IN.    -   If        -   Operation is PROXY_CACHEABLE_APP_COMPRESSED; or        -   Operation is PROXY_UNCACHEABLE_APP_COMPRESSED; and        -   SERVER_BYTES_IN>0;    -   Then replace CLIENT_BYTES_OUT with SERVER_BYTES_IN.    -   If        -   Operation is PROXY_CACHEABLE_APP_COMPRESSED; or        -   Operation is PROXY_UNCACHEABLE_APP_COMPRESSED; and        -   SERVER_BYTES_OUT>0;    -   Then replace CLIENT_BYTES_IN with SERVER_BYTES_OUT.    -   If        -   CLIENT_BYTES_IN>0; and        -   CLIENT_BYTES_OUT==0; and        -   SERVER_BYTES_IN==0; and        -   SERVER_BYTES_OUT==0;    -   Then replace CLIENT_BYTES_IN with zero value.    -   If RESPONSE_TIME<0 then replace RESPONSE_TIME with zero value.

Report Processing

Various example field calculations are now described.

Example Time on not Charging Calculation

FIG. 20 graphically illustrates an example calculation of theTIME_ON_NOT_CHARGING field. In some embodiments, a power log can be usedto make the calculation. In some embodiments, the TIME_ON_NOT_CHARGINGfield represents a sum of intervals when a device's battery health wasdecreasing. For example, TIME_ON_NOT_CHARGING=[Battery interval1]+[Battery interval 2].

Example Charge Drop Percent Calculation

FIG. 21 graphically illustrates an example calculation of theCHARGE_DROP_PERCENT field. Again, a power log can be used to make thecalculation. In some embodiments, the Drop percent represents a sum ofchanges of battery health when battery health is decreasing. Forexample, CHARGE_DROP_PERCENT=[89−25]+[60−10]=64+50=114 (percent).

Example Time Radio State Calculation

The table 22 below describes the relationship between various exampleprevious radio states and corresponding report fields. Morespecifically, the table below describes the TIME_RADIO_STATE_n fieldcalculation, where n runs from 1 to 10. In this example, a radio log isused and if the value of INTERVAL in the radio log is greater than zero,then the value of INTERVAL field is saved into one of theTIME_RADIO_STATE_n fields.

TABLE 22 Correspondence between previous radio states and report fields# Previous radio state Report field 1 DATA_ACTIVITY_CONNECTEDTIME_RADIO_STATE_1 2 DATA_ACTIVITY_DORMANT TIME_RADIO_STATE_2 3DATA_CONNECTED TIME_RADIO_STATE_3 4 DATA_CONNECTING TIME_RADIO_STATE_4 5DATA_DISCONNECTED TIME_RADIO_STATE_5 6 DATA_SUSPENDED TIME_RADIO_STATE_67 STATE_EMERGENCY_ONLY TIME_RADIO_STATE_7 8 STATE_IN_SERVICETIME_RADIO_STATE_8 9 STATE_OUT_OF_SERVICE TIME_RADIO_STATE_9 10STATE_POWER_OFF TIME_RADIO_STATE_10

Example Transition into Radio State Calculation

The table 23 below describes the TRANS_INTO_RADIO_STATE_n field, where nruns from 1 to 10. In this example, a radio log is used. TheTRANS_INTO_RADIO_STATE_n field represents the number of times a radiolog has transitioned into certain state.

TABLE 23 Correspondence between current radio states and report fieldsReport field (how many times # Current radio state radio was in thisstate) 1 DATA_ACTIVITY_CONNECTED TRANS_INTO_RADIO_STATE_1 2DATA_ACTIVITY_DORMANT TRANS_INTO_RADIO_STATE_2 3 DATA_CONNECTEDTRANS_INTO_RADIO_STATE_3 4 DATA_CONNECTING TRANS_INTO_RADIO_STATE_4 5DATA_DISCONNECTED TRANS_INTO_RADIO_STATE_5 6 DATA_SUSPENDEDTRANS_INTO_RADIO_STATE_6 7 STATE_EMERGENCY_ONLY TRANS_INTO_RADIO_STATE_78 STATE_IN_SERVICE TRANS_INTO_RADIO_STATE_8 9 STATE_OUT_OF_SERVICETRANS_INTO_RADIO_STATE_9 10 STATE_POWER_OFF TRANS_INTO_RADIO_STATE_10

Example WCDMA Time Calculation

The table 24 below describes the WCDMA_TIME_IN_DCH, WCDMA_TIME_IN_FACH,WCDMA_TIME_IN_PCH, and WCDMA_TIME_IN_IDLE fields. Again, a radio log isused in this example and if the value of INTERVAL in the radio log isgreater than zero, then the value of the INTERVAL field is saved intoone of fields WCDMA_TIME_< . . . > fields. The correspondence betweenprevious radio states and report fields is shown in the table below.

TABLE 24 Correspondence between previous radio states and report fields# Previous radio state Report field 1 CELL_DCH WCDMA_TIME_IN_DCH 2CELL_PCH WCDMA_TIME_IN_PCH 3 CELL_FACH WCDMA_TIME_IN_FACH 4 IDLEWCDMA_TIME_IN_IDLE

Example WCDMA Transition into Radio State Calculation

The table 25 below describes the WCDMA_TRANS_INTO_DCH,WCDMA_TRANS_INTO_FACH, WCDMA_TRANS_INTO_PCH, WCDMA_TRANS_INTO_IDLEcalculation fields. The radio log is used in this example. FieldWCDMA_TRANS_INTO_< . . . > indicates how many times radio log hastransition into certain state.

TABLE 25 Correspondence between current radio states and report fieldsReport field (how many times # Current radio state radio was in thisstate) 1 CELL_DCH WCDMA_TRANS_INTO_DCH 2 CELL_PCH WCDMA_TRANS_INTO_PCH 3CELL_FACH WCDMA_TRANS_INTO_FACH 4 IDLE WCDMA_TRANS_INTO_IDLE

Example Total Bytes Calculation

The table 26 below describes the TOTAL_BYTES_FROM_APP,TOTAL_BYTES_TO_APP, TOTAL_BYTES_FROM_NET, TOTAL_BYTES_TO_NET,TOTAL_BYTES_FROM_CACHE, TOTAL_BYTES_TO_CACHE calculation fields.

TABLE 26 Bytes calculation # Field in report Field from net log used forComment 1 TOTAL_BYTES_FROM_APP CLIENT_BYTES_IN Just copy value from netlog 2 TOTAL_BYTES_TO_APP CLIENT_BYTES_OUT 3 TOTAL_BYTES_FROM_NETSERVER_BYTES_IN 4 TOTAL_BYTES_TO_NET SERVER_BYTES_OUT 5TOTAL_BYTES_FROM_CACHE CACHED_BYTES_IN 6 TOTAL_BYTES_TO_CACHECACHED_BYTES_OUT

Example Total Hits Calculation

The table below 27 describes the TOTAL_HITS_FROM_APP, TOTAL_HITS_TO_APP,TOTAL_HITS_FROM_NET, TOTAL_HITS_TO_NET, TOTAL_HITS_FROM_CACHE, andTOTAL_HITS_TO_CACHE calculation fields.

TABLE 27 Hits calculation # Field in report Field from net log used forComment 1 TOTAL_HITS_FROM_APP CLIENT_BYTES_IN 1 if CLIENT_BYTES_IN > 0,0 2 TOTAL_HITS_TO_APP CLIENT_BYTES_OUT 1 if CLIENT_BYTES_OUT > 0, 0 3TOTAL_HITS_FROM_NET SERVER_BYTES_IN, 1 if SERVER_BYTES_IN > 0 or 4TOTAL_HITS_TO_NET SERVER_BYTES_OUT 1 if SERVER_BYTES_OUT > 0, 5TOTAL_HITS_FROM_CACHE CACHED_BYTES_IN 1 if CACHED_BYTES_IN > 0, 0 6TOTAL_HITS_TO_CACHE CACHED_BYTES_OUT 1 if CACHED_BYTES_OUT > 0,

Example Cache Requests. Bytes and Hits.

Table 28 below describes the TOTAL_BYTES_CACHE_REQ andTOTAL_HITS_CACHE_REQ calculation fields.

TABLE 28 Cache request calculation # Field in report Field from net logused for Comment 1 TOTAL_BYTES_CACHE_REQ CLIENT_BYTES_IN,CLIENT_BYTES_IN if 2 TOTAL_HITS_CACHE_REQ CLIENT_BYTES_IN, 1 ifCLIENT_BYTES_IN > 0 and

Example Connections Calculation

Table 29 below describes the SIM_RADIO_STATE_CHANGES_ACTUAL andSIM_RADIO_STATE_CHANGES_SAVED calculation fields. In some embodiments,the net log fields can be used to calculate these report fields. Thereare two cases in calculation:

-   -   Report key category is “Application”;    -   Report key category is other (not “Application”)

Example Report Key Category is “Application”

TABLE 30 Connections calculation # Field in report Field from net logused for Comment 1 SIM_RADIO_STATE_CHANGES_ACTUALSIM_ACTUAL_CONN_PER_APP Just copy 2 SIM_RADIO_STATE_CHANGES_SAVEDSIM_SAVED_CONN_PER_APP

Example Report Key Category is Other (not “Application”)

TABLE 31 Connections calculation # Field in report Field from net logused Comment 1 SIM_RADIO_STATE_CHANGES_ACTUAL ACTUAL_CONN Just copyvalue from net 2 SIM_RADIO_STATE_CHANGES_SAVED SAVED_CONN

Example Time Calculation

Table 32 below describes the SIM_RADIO_TIME_CONN_ACTUAL andSIM_RADIO_TIME_CONN_SAVED calculation fields. In some embodiments, thenet log fields are used to calculate these report fields. There are twocases in calculation:

-   -   Report key category is “Application”;    -   Report key category is other (not “Application”)

Example Report Key Category is “Application”

TABLE 33 Time calculation # Field in report Field from net log used forComment 1 SIM_RADIO_TIME_CONN_ACTUAL SIM_ACTUAL_TIME_PER_APP Just copyvalue from 2 SIM_RADIO_TIME_CONN_SAVED SIM_SAVED_TIME_PER_APP

Example Report Key Category is Other (not “Application”)

TABLE 34 Time calculation # Field in report Field from net log usedComment 1 SIM_RADIO_TIME_CONN_ACTUAL ACTUAL_TIME Just copy value fromnet log 2 SIM_RADIO_TIME_CONN_SAVED SAVED_TIME

Example Netlog Fields

TABLE 35 Net Log format # Name Type Derived 1 TIMESTAMP TIMEST N 2CLIENT_Z7TP_ADDRESS STRING N 3 TRANSACTION_TYPE STRING N 4 VERSION_IDINT N 5 CLIENT_BYTES_IN LONG N 6 CLIENT_BYTES_OUT LONG N 7SERVER_BYTES_IN LONG N 8 SERVER_BYTES_OUT LONG N 9 CACHE_BYTES_IN LONG N10 CACHE_BYTES_OUT LONG N 11 HOST STRING N 12 APPLICATION STRING N 13APP_STATUS STRING N 14 OPERATION STRING N 15 PROTnetwork optimizationOLSTRING N 16 INTERFACE STRING N 17 RESPONSE_TIME LONG N 18 REQUEST_IDLONG N 19 STATUS_CODE INT N 20 ERROR_CODE INT N 21 CONTENT_TYPE STRING N22 HEADER_LENGTH INT N 23 CONTENT_LENGTH LONG N 24 REQUEST_HASH STRING N25 RESPONSE_HASH STRING N 26 ANALYSIS STRING N 27 OPTIMIZATION INT N 28DESTINATION_PORT INT N 29 SUBSCRIPTION_ID INT N 30 PAYLOAD STRING N 31VIRTUAL_CONN INT Y 32 ACTUAL_CONN INT Y 33 SAVED_CONN INT Y 34VIRTUAL_TIME LONG Y 35 ACTUAL_TIME LONG Y 36 SAVED_TIME LONG Y 37SIM_VIRTUAL_CONN INT Y 38 SIM_ACTUAL_CONN INT Y 39 SIM_SAVED_CONN INT Y40 SIM_VIRTUAL_TIME LONG Y 41 SIM_ACTUAL_TIME LONG Y 42 SIM_SAVED_TIMELONG Y 43 SIM_VIRTUAL_CONN_PER_(—) INT Y 44 SIM_ACTUAL_CONN_PER_(—) INTY 45 SIM_SAVED_CONN_PER_AP INT Y 46 SIM_VIRTUAL_TIME_PER_(—) LONG Y 47SIM_ACTUAL_TIME_PER_A LONG Y 48 SIM_SAVED_TIME_PER_AP LONG Y 49SIM_VIRTUAL_CONN_PER_(—) INT Y 50 SIM_ACTUAL_CONNECTIO INT Y 51SIM_SAVED_CONN_PER_H INT Y 52 SIM_VIRTUAL_TIME_PER_(—) LONG Y 53SIM_ACTUAL_TIME_PER_H LONG Y 54 SIM_SAVED_TIME_PER_HO LONG Y

Example Report Format Fields

TABLE 36 report format # Name Type Derived 1 TIMESTAMP TIMESTAMP N 2Lnetwork optimizationAL_TIMESTAMP TIMESTAMP N 3 ENTITY_ID STRING N 4BEARER_TYPE INT N 5 CATEGORY_TYPE INT N 6 CATEGORY_VALUE STRING N 7TOTAL_BYTES_TO_APP LONG Y 8 TOTAL_BYTES_FROM_APP LONG Y 9TOTAL_BYTES_TO_CACHE LONG Y 10 TOTAL_BYTES_FROM_CACHE LONG Y 11TOTAL_BYTES_TO_NET LONG Y 12 TOTAL_BYTES_FROM_NET LONG Y 13TOTAL_BYTES_CACHE_REQ LONG Y 14 TOTAL_HITS_TO_APP INT Y 15TOTAL_HITS_FROM_APP INT Y 16 TOTAL_HITS_TO_CACHE INT Y 17TOTAL_HITS_FROM_CACHE INT Y 18 TOTAL_HITS_TO_NET INT Y 19TOTAL_HITS_FROM_NET INT Y 20 TOTAL_HITS_CACHE_REQ INT Y 21CHARGE_DROP_PERCENT INT Y 22 TIME_ON_NOT_CHARGING LONG Y 23TIME_RADIO_STATE_1 LONG Y 24 TIME_RADIO_STATE_2 LONG Y 25TIME_RADIO_STATE_3 LONG Y 26 TIME_RADIO_STATE_4 LONG Y 27TIME_RADIO_STATE_5 LONG Y 28 TIME_RADIO_STATE_6 LONG Y 29TIME_RADIO_STATE_7 LONG Y 30 TIME_RADIO_STATE_8 LONG Y 31TIME_RADIO_STATE_9 LONG Y 32 TIME_RADIO_STATE_10 LONG Y 33TRANS_INTO_RADIO_STATE_1 INT Y 34 TRANS_INTO_RADIO_STATE_2 INT Y 35TRANS_INTO_RADIO_STATE_3 INT Y 36 TRANS_INTO_RADIO_STATE_4 INT Y 37TRANS_INTO_RADIO_STATE_5 INT Y 38 TRANS_INTO_RADIO_STATE_6 INT Y 39TRANS_INTO_RADIO_STATE_7 INT Y 40 TRANS_INTO_RADIO_STATE_8 INT Y 41TRANS_INTO_RADIO_STATE_9 INT Y 42 TRANS_INTO_RADIO_STATE_10 INT Y 43RADIO_STATE_CHANGES_ACTUAL INT Y 44 RADIO_TIME_CONN_ACTUAL LONG Y 45RADIO_STATE_CHANGES_SAVED INT Y 46 RADIO_TIME_CONN_SAVED LONG Y 47SIM_RADIO_STATE_CHANGES_ACTUAL INT Y 48 SIM_RADIO_TIME_CONN_ACTUAL LONGY 49 SIM_RADIO_STATE_CHANGES_SAVED INT Y 50 SIM_RADIO_TIME_CONN_SAVEDLONG Y 51 WCDMA_TRANS_INTO_DCH INT Y 52 WCDMA_TRANS_INTO_FACH INT Y 53WCDMA_TRANS_INTO_PCH INT Y 54 WCDMA_TRANS_INTO_IDLE INT Y 55WCDMA_TIME_IN_DCH LONG Y 56 WCDMA_TIME_IN_FACH LONG Y 57WCDMA_TIME_IN_PCH LONG Y 58 WCDMA_TIME_IN_IDLE LONG Y 59NEW_SUBSCRIBER_COUNT INT Y 60 ACTIVE_SUBSCRIBER_COUNT INT Y 61RECURRING_HASH LONG Y

FIG. 22 illustrates various example measurement points from which alog/reporting data analysis core module such as, for example,log/reporting data analysis core 255 a of FIG. 2E or CRSC analysis core375 a of FIG. 3E, can perform measurements for modeling signals in thedata network. Some examples of the output metrics which can be adaptedby the log/reporting data analysis core module are listed in FIGS.23A-23E.

As discussed above, in some embodiments the a log/reporting dataanalysis core can make various calculations. For example, FIG. 24Agraphically illustrates an example of network optimization bytescalculations.

In this example, with respect to the HTTPS traffic, the networkoptimization's mock certificate is smaller than the certificate receivedfrom the network, which would appear as negative savings. For HTTPShandshakes, bytes-to-app are replaced with bytes-from-network. Withrespect to from-app-bytes only, the network optimization receives therequest irrespective of network availability. Most often caused bynetwork unavailability. In cases when request does not go out to networkand is not served from cache, adjust network optimization-AT-ADJ to 0.

In this example, the difference between (adjusted) Application (App) andNetwork Traffic is Saved Traffic (i.e., [networkoptimization-AT-ADJ]−[network optimization-NT]=[networkoptimization-ST]). The Saved Traffic plus the Total Network Traffic isthe total application traffic (i.e., [networkoptimization-ST]+[TNT]=[TAT]). The (adjusted) App Traffic divided by theTotal App Traffic is the Bytes Coverage (i.e., [networkoptimization-AT-ADJ]/[TAT]=BC.

In some embodiments, the network optimization bytes calculation'scoverage can be affected by:

-   -   Traffic channeled directly to Network Interface instead of        through: network optimization        -   TCP ports configured to bypass: IMAP, POP, 7TP        -   network optimization client in failover        -   3^(rd) party client reconfiguring traffic flows (typically            tethering)    -   Total Network Traffic recorded for incorrect interfaces:        Interfaces defined for TNT are configured manually. New device        models need to be verified.    -   Temporal factors: Total Network Traffic is recorded        periodically, while network optimization Application Traffic is        recorded for each transaction. Disruptions in data collection,        such as device reboot may cause different cut-off for these        metrics. Network change notifications may also appear in a        middle of a longer transaction, making it unclear which network        interface was used.    -   TCP/IP and UDP protocol overhead and TCP retransmissions:        measured for Total Network Traffic, but not measured for network        optimization Application Traffic.

FIGS. 24A-24J graphically illustrate various calculations of exampleoutput metrics that can be used in embodiments of the log/reporting dataanalysis core module.

FIG. 25 depicts an example diagram illustrating a general architecturaloverview of a distributed Network optimization system including themeasurement points from which a log/reporting data analysis core modulecan perform measurements for modeling signals in the data network. FIGS.26A-26N show additional examples of and/or alternative output metricsthat the log/reporting data analysis core module can adapt.

For example, FIGS. 26A-26C illustrate example Data Metrics. FIGS.26D-26F illustrate example Optimization Metrics. In some embodiments,exception can exist for ISnetwork optimization/ISOTC: Internal SignalingOptimization formulas do not use simulated values in by protocolcalculations. FIG. 26G illustrates example Users Metrics. In someembodiments, the user metrics can be used to provide the unique numberof users (e.g., as identified by 7TP address) or user combinationswithin the time period of concern. Additionally, the metrics may beuseful to calculate metrics like bytes/connections per user per day.FIG. 26H illustrates example Battery Metrics. FIGS. 26I-26K illustrateexample Signaling Metrics. For example, a Signaling Overall metric canprovide the number of state transitions within the time period ofconcern, broken by the radio state. Likewise, a Time Connected Overallcan provide the time connected within the time period of concern, brokenby the radio state. FIGS. 26L-26M illustrate example Dimensions Metrics.FIG. 26N illustrates example Optimization Metrics. FIG. 26N illustratesexample Optimization Metrics.

FIG. 27 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a user device, a tablet PC, a laptop computer, a set-topbox (STB), a personal digital assistant (PDA), a cellular telephone, aniPhone, an iPad, a Blackberry, a processor, a telephone, a webappliance, a network router, switch or bridge, a console, a hand-heldconsole, a (hand-held) gaming device, a music player, any portable,mobile, hand-held device, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer that, when read andexecuted by one or more processing units or processors in a computer,cause the computer to perform operations to execute elements involvingthe various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

Additional Embodiments

In some embodiments, a method of modeling signaling in a mobile networkis disclosed. The method includes: determining if transactions initiatedby mobile applications executing on a mobile device in the mobilenetwork cause network signaling requiring a corresponding radioconnection, wherein at least a portion of the network signaling causedby the transactions is filtered by a traffic optimization engine; andmodeling the network signaling for the mobile device based, at least inpart, on the filtered network signaling.

In some embodiments, the filtered network signaling does not cause acorresponding radio connection.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating signaling efficiency indicating atotal number of the radio connections that are saved as a result of thefiltering.

In some embodiments, calculating the signaling efficiency furthercomprises: accessing a radio log and a traffic activity log associatedwith the mobile device; modeling a quantity of virtual radio connectionsbased on the radio log and the traffic activity log, wherein the virtualradio connections indicate radio connections that would occur but forsaid filtering; determining a quantity of actual radio connections basedon the radio log, wherein the total number of the radio connections thatare saved comprises the difference between the quantity of virtual radioconnections and the quantity of actual radio connections.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating a time connected efficiencyindicating a total radio connection time saved as a result of thefiltering.

In some embodiments, calculating the time connected efficiency furthercomprises: accessing a radio log and a traffic activity log associatedwith the mobile device; modeling a virtual radio time connected based onthe radio log and the traffic activity log, wherein the virtual radiotime connected indicates an amount of time that the mobile device radiowould be active but for said filtering; determining an actual radio timeconnected based on the radio log, wherein the actual radio timeconnected indicates an amount of time that the mobile device radio isactive; wherein the total radio connection time saved comprises thedifference between the virtual radio time connected and the actual radiotime connected.

In some embodiments, the methods further comprise tracking thetransactions initiated by the mobile applications executing on themobile device in the mobile network.

In some embodiments, the methods further comprise applying, by thetraffic optimization engine, a traffic optimization technique to filterthe network signaling such that at least the portion of the networksignaling is filtered.

In some embodiments, the methods further comprise accessing trafficactivity logs indicating traffic metrics measured at multiple trafficmeasurement points in the mobile device, wherein modeling the networksignaling further comprises calculating a connection status and a timeconnected interval based on the traffic metrics.

In some embodiments, modeling the network signaling for the mobiledevice further comprises attributing the network signaling to individualapplications of the mobile applications executing on the mobile device.

In some embodiments, the methods further comprise accessing a radio logand a traffic activity log associated with the mobile device, whereinthe radio log indicates a state of a mobile device radio, wherein thetraffic activity log indicates various traffic metrics measured atmultiple measurement points in the mobile device; and maintaining thetraffic activity log by calculating one or more log/reporting datafields.

In some embodiments, modeling the network signaling based on the one ormore log/reporting data fields.

In some embodiments, maintaining the traffic activity log comprises longpolling.

In some embodiments, the one or more log/reporting data fields aredivided into connection flag fields and time connected count fields.

In some embodiments, the one or more log/reporting data fields arecategorized into one or more of the following categories: actual,simulated, actual simulated, virtual simulated, actual simulated perapplication, and virtual simulated per application.

In some embodiments, the traffic optimization engine comprises one ormore elements of a distributed caching and proxy system.

In some embodiments, the distributed caching and proxy system includes aproxy client and a proxy server.

In some embodiments, the filtered radio connections are cached locallyby the proxy client.

In some embodiments, a method of modeling network signaling in a mobilenetwork is disclosed. The method comprises: accessing a radio logassociated with a mobile device operating in the mobile network, theradio log indicating a state of a mobile device radio; accessing atraffic activity log associated with the mobile device, the trafficactivity log indicating various traffic metrics measured at multiplemeasurement points in the mobile device; calculating one or morelog/reporting data fields based on one or more of the radio log and thetraffic activity log; and modeling the network signaling for the mobiledevice based on the one or more log/reporting data fields.

In some embodiments, the method further comprises determining iftransactions initiated by mobile applications executing on the mobiledevice in the mobile network cause network signaling requiring acorresponding radio connection on the mobile device, wherein at least aportion of the network signaling caused by the transactions is filteredby a traffic optimization engine.

In some embodiments, the filtered network signaling does not cause acorresponding radio connection on the mobile device and the unfilterednetwork signaling does cause corresponding radio connection on themobile device.

In some embodiments, modeling the network signaling further comprisescalculating a connection status and a time connected interval based, atleast in part, on the one or more calculated log/reporting data fields.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating signaling efficiency indicating atotal number of the radio connections that are saved as a result of thefiltering.

In some embodiments, calculating the signaling efficiency furthercomprises: accessing a radio log and a traffic activity log associatedwith the mobile device; modeling a quantity of virtual radio connectionsbased on the radio log and the traffic activity log, wherein the virtualradio connections indicate radio connections that would occur but forsaid filtering; determining a quantity of actual radio connections basedon the radio log, wherein the total number of the radio connectionscomprises the difference between the quantity of virtual radioconnections and the quantity of actual radio connections.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating a time connected efficiencyindicating a total radio connection time saved as a result of thefiltering.

In some embodiments, calculating the time connected efficiency furthercomprises: accessing a radio log and a traffic activity log associatedwith the mobile device; modeling a virtual radio time connected based onthe radio log and the traffic activity log, wherein the virtual radiotime connected indicates an amount of time that the mobile device radiowould be active but for said filtering; determining an actual radio timeconnected based on the radio log, wherein the actual radio timeconnected indicates an amount of time that the mobile device radio isactive; wherein the total radio connection time saved comprises thedifference between the virtual radio time connected and the actual radiotime connected.

In some embodiments, the methods further comprise maintaining thetraffic activity log by tracking transactions and measuring the varioustraffic metrics at the multiple measurement points in the mobile device.

In some embodiments, the one or more log/reporting data fields aredivided into connection flag fields and time connected count fields.

In some embodiments, the one or more log/reporting data fields arecategorized into one or more of the following categories: actual,simulated, actual simulated, virtual simulated, actual simulated perapplication, and virtual simulated per application.

In some embodiments, a mobile device is disclosed. The mobile devicecomprise a radio; a processor; and a memory storing instruction, whereinthe instructions, when executed by the processor, causes the mobiledevice to: access a radio log associated with a mobile device operatingin the mobile network, the radio log indicating a state of a mobiledevice radio; access a traffic activity log associated with the mobiledevice, the traffic activity log indicating various traffic metricsmeasured at multiple measurement points in the mobile device; calculateone or more log/reporting data fields based on one or more of the radiolog and the traffic activity log; and model the network signaling forthe mobile device based on the one or more log/reporting data fields.

In some embodiments, wherein the instructions, when executed by theprocessor, further causes the mobile device to: determine iftransactions initiated by mobile applications executing on the mobiledevice in the mobile network cause network signaling requiring acorresponding radio connection on the mobile device, wherein at least aportion of the network signaling caused by the transactions is filteredby a traffic optimization engine, wherein the filtered network signalingdoes not cause a corresponding radio connection on the mobile device andthe unfiltered network signaling does cause corresponding radioconnection on the mobile device.

In some embodiments, the mobile further comprises a traffic optimizationengine comprising one or more elements of a distributed caching andproxy system.

In some embodiments, the distributed caching and proxy system includes aproxy client and a proxy server, and wherein the filtered radioconnections are cached locally by the proxy client.

In some embodiments, the instructions, when executed by the processor,further causes the mobile device to: track transactions initiated bymobile applications executing on the mobile device in the mobilenetwork; measure the various traffic metrics at the multiple measurementpoints in the mobile device; and maintain the traffic activity log basedon the measurements.

In some embodiments, to model the network signaling for the mobiledevice, the instructions, when executed by the processor, further causesthe mobile device to calculate signaling efficiency indicating a totalnumber of the radio connections that are saved as a result of thefiltering.

In some embodiments, to calculate the signaling efficiency, theinstructions, when executed by the processor, further causes the mobiledevice to: access a radio log and a traffic activity log associated withthe mobile device; model a quantity of virtual radio connections basedon the radio log and the traffic activity log, wherein the virtual radioconnections indicate radio connections that would occur but for saidfiltering; determine a quantity of actual radio connections based on theradio log, wherein the total number of the radio connections comprisesthe difference between the quantity of virtual radio connections and thequantity of actual radio connections.

In some embodiments, to model the network signaling for the mobiledevice, the instructions, when executed by the processor, further causesthe mobile device to calculate a time connected efficiency indicating atotal radio connection time saved as a result of the filtering.

In some embodiments, to calculate the time connected efficiency, theinstructions, when executed by the processor, further causes the mobiledevice to: access a radio log and a traffic activity log associated withthe mobile device; model a virtual radio time connected based on theradio log and the traffic activity log, wherein the virtual radio timeconnected indicates an amount of time that the mobile device radio wouldbe active but for said filtering; determine an actual radio timeconnected based on the radio log, wherein the actual radio timeconnected indicates an amount of time that the mobile device radio isactive; wherein the total radio connection time saved comprises thedifference between the virtual radio time connected and the actual radiotime connected.

In some embodiments, a computer-readable storage medium storinginstructions to be implemented by a mobile device having a processor isthe discloses. The instructions, when executed by the processor, causesthe mobile device to: determine if transactions initiated by mobileapplications executing on the mobile device in a mobile network causenetwork signaling requiring a corresponding radio connection, wherein atleast a portion of the network signaling caused by the transactions isfiltered by a traffic optimization engine, wherein the filtered networksignaling does not cause a corresponding radio connection; and modelingthe network signaling for the mobile device based, at least in part, onthe filtered network signaling.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating signaling efficiency indicating atotal number of the radio connections that are saved as a result of thefiltering.

In some embodiments, modeling the network signaling for the mobiledevice further comprises calculating a time connected efficiencyindicating a total radio connection time saved as a result of thefiltering.

In some embodiments, the instructions, when executed by the processor,further cause the processor to: access a radio log and a trafficactivity log associated with the mobile device, wherein the radio logindicates a state of a mobile device radio, wherein the traffic activitylog indicates various traffic metrics measured at multiple measurementpoints in the mobile device; and maintain the traffic activity log bycalculating one or more log/reporting data fields.

In some embodiments, modeling the network signaling is based on the oneor more log/reporting data fields.

As used herein, Network optimization or network optimization refers toone or more applications and systems that may be embodied on anyappropriate hardware that are configured for optimizing network trafficmanagement. For simplicity of discussion, “optimization” may refer tosystems or methods that utilize network optimization architecturedescribed herein and/or the filtering of network signaling sent orreceived by a mobile device in order to reduce usage of a cellular radioof the mobile device.

The one or more applications and associated user interfaces disclosedherein may display a number of ‘minutes’ extended due to optimization.Additionally, the UI can display both the number of minutes (‘minutes’)used as well as the ‘minutes’ extended due to optimization for eachoptimized application.

A table will be maintained which will contain the data to be displayed.The structure of the table should ensure minimum latency/delay on UI.For performance issues, it will be good to have some of the informationpre-calculated.

In some embodiments, data may be inserted on every traffic log capture.Database may keep data for the most recent seven days.

Schema

In one or more embodiments, data may be stored in one or more tables. Inone embodiment, there may be two tables. A first table may contain theoverall statistics. This may be termed table ‘O’. Another table may haveapplication level data. This may be termed table ‘A’. The UI may accesstable ‘O’ for displaying overall metric and table ‘A’ for showing applevel metrics.

Table ‘O’ may include each of, all of, or a combination of the followingcolumns: Date time, Battery Level, Charging Status, Idle time, and TimeConnected.

Table ‘A’ may include each of, all of, or a combination of the followingcolumns: Date and/or time, Application, Bytes usage (expressed as apercentage) for each application, Time Connected (calculated for eachapp)**, Amount optimizing for each app, and Saved Time Connected(calculated value for each app)**. ** It may be appreciated that theseparameters can be calculated as needed or for each interval andpopulated into the DB. The former option makes for a smaller/simpler DB;the second leaves fewer calculations to be performed when needed (i.e.,to show a result to the user in the UI).

Definition of Idle period

The mobile device may be associated with an idle period when thefollowing conditions are mat: Screen off, Device on mobile network (noton Wi-Fi), No call, No GPS, Audio not playing.

Logic

Table ‘O’

Date and/or time may represent the timestamp of that event. The eventwill be in sync with traffic log events. Battery level may representbattery level at that timestamp. Charging Status may represent a Booleanvalue indicating if the device is on charger or not. Idle time mayrepresent cumulative time from last timestamp when the device is idle.Time Connected may represent cumulative time (for the entire duration,not when idle only) from last timestamp (or from the last devicereboot).

Table ‘A’

Date and/or Time may represent the timestamp of that event. The eventmay be in sync with traffic log events. Should be same as in table ‘O’.Application may represent application name.

Bytes usage may represent share of bytes for a particular app since lasttimestamp. Netlog may be used to get app bytes and traffic logs to gettotal bytes. Bytes usage may represent app bytes/total bytes.Application Time Connected** may be a calculated field. Table ‘A’ may bejoined to table ‘O’ per the timestamp. It is calculated as bytesusage*time connected (from table ‘O’). One option includes to store timeconnected (from table ‘O’) in a variable and use that variable tocalculate the values before inserting in table ‘A’.

Amount optimizing for each application may represent number of units;where ‘unit’ defines the measurement unit for savings for eachapplication. The unit will be different for each of the optimizationstrategies used. (to start, it will only be time connected for that app,but may need more than one variable).

-   -   Saved Time Connected**—will be a calculated field. It will be        calculated as    -   ‘Amount optimizing for each app’ *a (defined under parameters)

Actions to be Performed at the End of Each Day or Other PredeterminedTime Period

In one or more embodiments, the data will be aggregated at appropriatelevels

Parameters

The model will be populated with pre-determined parameters mentionedbelow—

β—Power consumption associated with Time Connected (TC)

I_(oh)—represents the offset (in terms of current) for a given amount oftime connected.

Function to sum per app savings. Use ‘sum’ simply to start with.

α_(appn)=network optimization of time connected for application ‘n’ inpercentage

Aggregation

According to some embodiments, previous data may be aggregated at anylevel (e.g., daily).

Calculation Algorithms for Battery Optimization

Battery Improvement Display Parameters

In one or more embodiments, parameters to be displayed to the user ofthe one or more applications embodied on the one or more hardwaredevices disclosed herein are the following:

‘x’ minutes extended

‘x’ minutes used for each optimized application

‘x’ minutes extended for each optimized application

The first parameter is to be displayed as a single overall number, andthe latter two are broken down per optimized application. This needs tobe able to be shown to a given user at any time throughout the day, andshould represent the accumulated savings for the past 24 hours (i.e., arolling 24 hour timeframe).

The calculation for the ‘x’ minutes extended is as follows:

-   -   T_(extended)=T_(saved)

where

T_(extended)=The calculated time (in idle time minutes) that the devicebattery life has been extended due to network optimization

and

T_(saved)=The calculated time saved (in idle time minutes) due tonetwork optimization.

The time saved is calculated as follows:

T_(saved)=ΣT_(saved-appn), for n=1 to total number of optimized apps onthe device

Where

T_(saved-appn)=The calculated time saved (in idle time minutes) forapplication ‘n’ due to network optimization.

The per-application time saved is calculated as:

-   -   T_(saved-appn)=E_(saved-appn)/I_(idle)

Where

-   -   E_(saved-appn)=β*TC_(saved-appn)

β=The power consumption associated with Time Connected (Predefinedvalue)

and

TC_(saved-appn) is the saved time connected for application ‘n,’ and iscalculated as:

-   -   TC_(saved-appn)=[α_(appn)/(1−α_(appn))]* TC_(appn)

Where

α_(appn)=The network optimization of time connected for application ‘n’in percentage (Predefined value;TC_(saved-appn)=α_(appn)*TC_(without-network optimization))

and

TC_(appn) Represents the allocated (i.e., calculated) time connected forapplication ‘n,’ based on the relative proportion of bytes for eachapplication (calculated)

-   -   TC_(appn)=TC_(idle)*[B_(appn)/B_(idle)]

where

TC_(idle)=Total time connected when device is idle (measured)

B_(appn)=Network bytes for application ‘n’ (measured)

B_(idle)=Total network bytes when device is idle (measured)

Idle times are defined as the periods in which the following is true:

Screen is off

Device on mobile network (not on wifi)

Audio is not playing

No phone calls

No GPS use

The second display parameter (“‘x’ minutes used for each optimizedapplication”) is calculated as follows:

-   -   T_(appn)=E_(appn)/I_(Idle)

Where

-   -   E_(appn)=β*TC_(appn)

And β and TC_(appn) are already defined above.

The third display parameter (“‘x’ minutes extended for each optimizedapplication”) is simply

-   -   T_(saved-appn)

As defined above.

It has been determined that there seems to be a small change in therelationship between time connected and energy when network optimizationis optimizing (vs. not). This may be mostly accounted for by theadditional overhead associated with network optimization. If/when thisfactor is included, the overall energy saved looks like this:

-   -   E_(saved-appn)=Σβ*[TC_(saved-appn)]−I_(oh)*T_(idle)

Where

I_(oh) represents the offset (in terms of current) for a given amount oftime connected

and

T_(idle) represents the accumulated idle time

The determination of applicable β value(s) to relate power consumptionwith time connected is one of the dependencies for building a reasonablyaccurate battery savings model.

FIG. 28 shows the relationship between the effective current draw andtime connected as a portion of total time for sample WCDMA test runs.For the purposes of this model, some side-by-side tests are used,starting with WCDMA test runs. For these runs, the relationship betweenthe effective current draw and time connected (as a portion of totaltime) is shown in FIG. 28, both for devices with and without the one ormore applications disclosed herein (disclosed as network optimizationfor the one or more applications in the chart and depicted with thediamond shape and the non-network optimization version depicted with thesquare shape). In FIG. 28, the y-intercept for both lines was veryclose, and therefore was fixed to be the same value (6.5). The resultingslopes of the regression lines are indicative of the relationshipbetween the effective current draw and time connected. In this case, theslope for no network optimization (i.e., without optimization) is 165,whereas the network optimization data (i.e., with optimization) is 50%higher, at 246.

FIG. 29 shows the same data as FIG. 28 but evaluated in terms of pairsof results including one with optimization and one without optimization.The lines represent the effect of network optimization in terms of bothmA and time connected, where the upper right points represent the devicewithout network optimization and the lower left reflect the device withnetwork optimization optimization. In FIG. 29, the dashed lines in thegraph are the same regression lines as shown in the prior graph. Thelegend shows three values for each data set: SbS test run (Jira) number,the percentage of optimization in terms of time connected (overall alphafor each application set), and the slope of the resulting lines(ΔmA/ΔTC). The thickest line shows the aggregated result for all thisentire data set. When determining the net savings on energy consumptiondue to network optimization, the slopes of these data pairs provide themost valuable information for establishing β. Since the amount of savedtime connected from network optimization is inferred, and then using βto translate that into saved energy, these slopes matter most.

One aspect of these results is that the effect of network optimizationon the savings in terms of mA versus savings in TC is quite consistentfrom run to run (i.e., the slopes are very similar). Note that theslopes follow neither the “No network optimization” nor the “networkoptimization” regression line, and is noticeably shallower than either.This is qualitatively as expected. If network optimization had no impacton energy usage other than reducing time connected, then one wouldexpect to move down the “No network optimization” regression line asnetwork optimization optimizes time connected. However, it is known thatnetwork optimization has an additional impact on the energy consumptionbecause of what it does and how it does it. This results in a slightlydiminished savings in terms of mA for a given amount of saved timeconnected, or a slightly less steep slope for each of the lines.

FIG. 30 shows a cross-plot of the slopes (β) versus the TC optimizations(overall alpha) for SbS runs reflecting varying amounts of optimization.These SbS runs reflect varying amounts of network optimization (in termof TC saved, or overall alpha values). To understand if there is asystematic relationship between the slopes of the lines above and thenet optimization amounts, FIG. 30 is a cross-plot of the slopes (β)versus the TC optimizations (overall alpha). FIG. 30 shows arelationship between β and α. If the overhead associated withoptimizations (in terms of an offset in mA) were a constant,irrespective of the amount of TC optimization, one would expect the β todecline as α declines since the relative impact of that overhead wouldincrease.

FIG. 31 shows a cross-plot of overhead versus a according to anembodiment of the subject matter described herein. To assess whether ornot the optimization “overhead” is constant, this overhead value iscalculated as the difference between the mA for the network optimizationoptimizing data point versus the associated mA by moving along the slopeof the “NO network optimization” regression line. The resulting overheadversus α looks like FIG. 31. FIG. 31 shows the “overhead to be on theorder of 1.5 mA to as large as 6.5 mA, with a slight (negative)correlation with α. Based on this, how does one determine an effective βto use in client V4.0? There are three options to be considered here:

Define and use a constant β value (such as the aggregated value of 112that comes the data sets above). Create a simple equation for β as afunction of the overall α (such as β=114.8*α+37.25) from above. Definean overhead (either fixed or variable value) in terms of mA for networkoptimization, and use the “No network optimization regression” line asthe primary relationship between mA saved versus TC saved. This overheadwould then be subtracted from the calculated mA saved using the NOnetwork optimization regression relationship (which is the same asadding this overhead value to the calculated mA for network optimizationoptimized results).

Comparison with Prior SbS Battery Modeling Results

In one or more experiments, an analysis of SbS battery runs was carriedout and it derived the following equations:

WCDMA:

battery consumption (mAh)=165.1201*time connected (hours)−0.8732oc_status (on or off)+7.5975 [R²=0.85]

LTE:

battery consumption (mAh)=103.8353*time connected (hours)+2.0123oc_status (on or off)+2.3717 [R²=0.75]

The key factor here is to point out that his analysis of WCDMA SbS runsyields an effective coefficient of 165 (mAh per hour time connected; orunits of mA). This is reasonably close to the value derived above of112, and one could consider using the higher (less conservative) valueof 165, given that Andy's analysis was across a broader set of data thanwas used in the analysis above.

Comparison with Customer Trials Results

A qualitative comparison to the recent customer trials results follows.

The trials results have not yet been analyzed for the idle only periods.Hence, the results reflect both the active and the idle periods, and thescreen on effects are not factored in. So the resulting correlation withtime connected overstates the effect of time connected (because duringactive periods, a large proportion of the battery usage is associatedwith the display, although it is lumped into the time connectedparameter here.

Another key factor in differences here are that the trials were run on avariety of networks, and used 2G, 3G, and LTE during the trial periods.These effects are also not being accounted for here.

FIG. 32 shows a plot of slopes resulting from no-optimization versusoptimization in the same graphical representation as used in FIG. 30.The resulting lines of no network optimization versus networkoptimization optimizing (from the A/B periods in the trials) are shownin FIG. 32 in the same graphical representation as used above for theSbS results. Note that the values of mA are much higher than those seenduring idle only periods of SbS, again suggesting that the effects ofscreen on during active periods have a significant impact on theresults.

The resulting slopes from this are as follows:

coefficient Trial 1 42.37 Trial 2 419.64 Trial 3 210.85 Trial 4 33.98Trial 5 −73.12 Trial 6 414.86 Trial 7 201.37 Trial 8 160.40 Trial 9684.16 Aggregated overall 407.12

The aggregated result suggests a coefficient approximately 400 (versusthe 112 or so derived above). Once again, the key difference here isthat the customer trials data has not excluded the active (or screen on)periods, and hence the coefficients are known to be too high. This valueof 400 represents an upper bound and provides a qualitative comparisonthat might be useful.

The subject matter described herein includes estimating the savingsassociated with performing network optimization without calculatingvirtual connections saved over a time period.

According to one aspect, a ‘saved radio connection time’ or ‘saved timeconnected’ is calculated. The mechanism may depend on the optimizationstrategy used. Various alternative optimization strategies and methodsfor calculating an amount of radio connection time saved are describedbelow.

In one example, saved time connected may include the actual timeconnected*alpha, where alpha is an application/strategy specific factor.The actual time connected may be divided between applications with analgorithm. The algorithm may divide the total time connected betweenapplications using the ratio of bytes used by each application.

In another example, saved time connected may include the ‘elapsed timewhen optimizing’*alpha, where alpha is an application/strategy specificfactor.

In another example, saved time connected may include the ‘number ofoptimization event's*dormancy timer*alpha, where alpha isapplication/strategy specific factor. It is appreciated that alpha mayreflect that not all optimization events result in savings.

In another example, saved time connected may include (number ofclient-side keepalives*k1−number of server-side keepalives)*dormancytimer. k1 and k2 may include factors to translate from keepalive eventsto connections. For example, k1 may include a reduction factor forclient-side keep alive message that would not have caused a connectionanyway. k2 may include a reduction of server-side keep alive messagesthat did not cause connections, including messages that are notradio-aligned.

According to another aspect, the saved radio connection time may befurther translated to saved/extended battery life by converting thesaved time connected to saved energy, and using energy-per-TC andidle-energy to calculate extension in battery life. Several examples ofthis calculation are provided below.

As used herein, power consumption [W] can be divided by voltage [V] andexpressed in [mA], where W refers to Watt, J refers to Joule, V refersto Volt, s refers to second, and mA refers to milliampere. In oneexample, saved/extended battery time [s] is equal to saved energy[J]/average power consumption when idle [W], where saved energy [J] isequal to savedTC (calculated, [s])*extra-power-per-time connected [TC](parameter, [W]), and average power consumption when idle [W] is equalto extra-power-per-TC [W] *ratio of TC of total idle time (calculated,no unit)+average non-TC power consumption (parameter, [W]).

In one embodiment, the mobile device may use a current fixed at 3.6V(this may differ depending on the device). In other words, some powerconsumption parameters may be 3.6 times larger than if they would havebeen expressed in Watts.

It may be appreciated that the parameters described herein may becalculated for WCDMA and/or LTE, and are device and network dependent.

In another example, extra-power-per-TC may be equal to the powerconsumption above the non-TC power consumption when device is otherwiseidle. Conversely, the non-TC power consumption may be equal to theaverage power consumption when radio is not on, but device is idle.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps, or employsystems having blocks, in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or sub-combinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times. Further any specific numbersnoted herein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further embodiments of thedisclosure.

These and other changes can be made to the disclosure in light of theabove Detailed Description. While the above description describescertain embodiments of the disclosure, and describes the best modecontemplated, no matter how detailed the above appears in text, theteachings can be practiced in many ways. Details of the system may varyconsiderably in its implementation details, while still beingencompassed by the subject matter disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the disclosure should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the disclosure with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the disclosure to the specific embodimentsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe disclosure encompasses not only the disclosed embodiments, but alsoall equivalent ways of practicing or implementing the disclosure underthe claims presented herein or presented in relation to any applicationclaiming priority hereto.

While certain aspects of the disclosure are presented, the inventorscontemplate the various aspects of the disclosure in any number of claimforms. For example, while only one aspect of the disclosure is recitedas a means-plus-function claim under 35 U.S.C. §112, ¶16, other aspectsmay likewise be embodied as a means-plus-function claim, or in otherforms, such as being embodied in a computer-readable medium. (Any claimsintended to be treated under 35 U.S.C. §112, ¶16 will begin with thewords “means for.”) Accordingly, the applicant reserves the right to addadditional claims after filing the application to pursue such additionalclaim forms for other aspects of the disclosure.

What is claimed is:
 1. A method comprising: filtering at least a portionof network signaling associated with mobile applications executing on amobile device; calculating an amount of time a radio connection isactive that is attributable to at least one mobile application;converting the calculated amount of time to estimated battery usage forthe at least one mobile application; calculating an expected batterydrain rate of the mobile device based on the estimated battery usage ofthe at least one mobile application; calculating an expected batterylife of the mobile device based on the expected battery drain rate and atotal battery capacity of the mobile device.
 2. The method of claim 1,wherein calculating the amount of time a radio connection is active thatis attributable to the at least one mobile application is based ontiming of data packets sent and received by the mobile application. 3.The method of claim 1, wherein calculating the amount of time a radioconnection is active that is attributable to the at least one mobileapplication is based on the amount of time radio connections associatedwith the mobile application are active.
 4. The method of claim 1,further comprising displaying, via a user interface, the calculatedamount of time.
 5. The method of claim 1, further comprising displaying,via a user interface, the estimated battery usage for the at least onemobile application.
 6. The method of claim 1, further comprisingdisplaying, via a user interface, the expected battery life of themobile device.
 7. A mobile device comprising: a memory and a processorconfigured for: filtering at least a portion of network signalingassociated with mobile applications executing on a mobile device;calculating an amount of time a radio connection is active that isattributable to at least one mobile application; converting thecalculated amount of time to estimated battery usage for the at leastone mobile application; calculating an expected battery drain rate ofthe mobile device based on the estimated battery usage of the at leastone mobile application; calculating expected battery life of the mobiledevice based on the expected battery drain rate and a total batterycapacity of the mobile device.
 8. The mobile device of claim 7, whereinthe processor is further configured for calculating the amount of time aradio connection is active that is attributable to the at least onemobile application based on the timing of data packets sent and receivedby the mobile application.
 9. The mobile device of claim 7, wherein theprocessor is further configured for calculating the amount of time aradio connection is active that is attributable to the at least onemobile application based on the amount of time radio connectionsassociated with the mobile application are active.
 10. The mobile deviceof claim 7, wherein the processor is further configured for displaying,via a user interface, the calculated amount of time.
 11. The mobiledevice of claim 7, wherein the processor is further configured fordisplaying, via a user interface, the estimated battery usage for the atleast one mobile application.
 12. The mobile device of claim 7, whereinthe processor is further configured for displaying, via a userinterface, the expected battery life of the mobile device.
 13. Anon-transitory computer-readable storage medium storing instructions tobe implemented by a mobile device having a processor, wherein theinstructions, when executed by the processor, causes the mobile deviceto: filter at least a portion of network signaling associated withmobile applications executing on a mobile device; calculate an amount oftime a radio connection is active that is attributable to at least onemobile application; convert the calculated amount of time to estimatedbattery usage for the at least one mobile application; calculate anexpected battery drain rate of the mobile device based on the estimatedbattery usage of the at least one mobile application; calculate expectedbattery life of the mobile device based on the expected battery drainrate and a total battery capacity of the mobile device.
 14. Thenon-transitory computer-readable storage medium of claim 13, wherein theinstructions, when executed by the processor, further causes the mobiledevice to calculate the amount of time a radio connection is active thatis attributable to the at least one mobile application based on thetiming of data packets sent and received by the mobile application. 15.The non-transitory computer-readable storage medium of claim 13, whereinthe instructions, when executed by the processor, further causes themobile device to calculate the amount of time a radio connection isactive that is attributable to the at least one mobile application basedon the amount of time radio connections associated with the mobileapplication are active.
 16. The non-transitory computer-readable storagemedium of claim 13, wherein the instructions, when executed by theprocessor, further causes the mobile device to display, via a userinterface, the calculated amount of time.
 17. The non-transitorycomputer-readable storage medium of claim 13, wherein the instructions,when executed by the processor, further causes the mobile device todisplay, via a user interface, the estimated battery usage for the atleast one mobile application.
 18. The non-transitory computer-readablestorage medium of claim 13, wherein the instructions, when executed bythe processor, further causes the mobile device to display, via a userinterface, the expected battery life of the mobile device.